14.2.2.2 High Dimensional Data, Hyperspectral Data, Hyper-Spectral Data Classification

Chapter Contents (Back)
Hyperspectral. See also Hyperspectral Data, Dimensionality Reduction, Band Selection. See also Hyperspectral Data, Endmember Extraction.

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PR(42), No. 7, July 2009, pp. 1193-1209.
Elsevier DOI 0903
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And: Corrigendum: PR(45), No. 9, September 2012, pp. 3580-3582.
Elsevier DOI 1206
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Earlier:
A novel validity measure for clusters of arbitrary shapes and densities,
ICPR08(1-4).
IEEE DOI 0812
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And:
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Springer DOI 0806
Clustering; Dynamic model; Arbitrary shaped clusters; Arbitrary density clusters; High dimensional data; Distance-relatedness BibRef

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Hoffbeck, J.P., Landgrebe, D.A.,
Classification of Remote Sensing Images Having High Spectral Resolution,
RSE(57), No. 3, September 1996, pp. 119-126. 9609
Hyperspectral. Use the techniques of chemistry spectroscopy for remotely sensed data.
PDF File. BibRef

Jimenez, L.O.[Luis O.], and Landgrebe, D.A.[David A.],
Supervised Classification in High-Dimensional Space: Geometrical, Statistical, and Asymptotical Properties of Multivariate Data,
SMC-C(28), No. 1, February 1998, pp. 39-54. 9806
Hyperspectral.
PDF File. BibRef

Jimenez, L.O., Landgrebe, D.A.,
Hyperspectral Data Analysis and Supervised Feature Reduction Via Projection Pursuit,
GeoRS(7), No. 6, November 1999, pp. 2653.
IEEE Top Reference. 9911
BibRef

Haertel, V., Landgrebe, D.A.,
On the Classification of Classes with Nearly Equal Spectral Response in Remote Sensing Hyperspectral Image Data,
GeoRS(37), No. 5, September 1999, pp. 2374.
IEEE Top Reference. BibRef 9909

Jackson, Q., Landgrebe, D.A.,
An adaptive classifier design for high-dimensional data analysis with a limited training data set,
GeoRS(39), No. 12, December 2001, pp. 2664-2679.
IEEE Top Reference. 0201
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Jackson, Q., Landgrebe, D.A.,
An adaptive method for combined covariance estimation and classification,
GeoRS(40), No. 5, May 2002, pp. 1082-1087.
IEEE Top Reference. 0206
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Nascimento, S.M.C., Ferreira, F., and Foster, D.H.,
Statistics of spatial cone-excitation ratios in natural scenes,
JOSA-A(19), No. 8, August 2002, pp. 1484-1490.
PDF File. Dataset, Hyperspectral.
HTML Version. BibRef 0208

Foster, D.H., Nascimento, S.M.C., Amano, K.,
Information limits on neural identification of coloured surfaces in natural scenes,
Visual Neuroscience(21), 2004, pp. 331-336.
PDF File. Dataset, Hyperspectral.
HTML Version. BibRef 0400

Marín-Franch, I.[Iván], Foster, D.H.[David H.],
Estimating Information from Image Colors: An Application to Digital Cameras and Natural Scenes,
PAMI(35), No. 1, January 2013, pp. 78-91.
IEEE DOI 1212
How much is there in the colors alone. BibRef

Kim, B., and Landgrebe, D.A.,
Hierarchical Classifier Design in High Dimensional, Numerous Class Cases,
GeoRS(29), No. 4, July 1991, pp. 518-528.
IEEE Top Reference. BibRef 9107

Dundar, M.M., Landgrebe, D.A.,
A model-based mixture-supervised classification approach in hyperspectral data analysis,
GeoRS(40), No. 12, December 2002, pp. 2692-2699.
IEEE Top Reference. 0301
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Madhok, V., Landgrebe, D.A.,
A process model for remote sensing data analysis,
GeoRS(40), No. 3, March 2002, pp. 680-686.
IEEE Top Reference. 0206
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Kuo, B.C., Landgrebe, D.A.,
A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction,
GeoRS(40), No. 11, November 2002, pp. 2486-2494.
IEEE Top Reference. 0301
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Dundar, M.M., Landgrebe, D.A.,
A Cost-Effective Semisupervised Classifier Approach With Kernels,
GeoRS(42), No. 1, January 2004, pp. 264-270.
IEEE Abstract. 0402
BibRef

Dundar, M.M., Landgrebe, D.A.,
Toward an Optimal Supervised Classifier for the Analysis of Hyperspectral Data,
GeoRS(42), No. 1, January 2004, pp. 271-277.
IEEE Abstract. 0402
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Nene, S.A.[Sameer A.], Nayar, S.K.[Shree K.],
A Simple Algorithm for Nearest-Neighbor Search in High Dimensions,
PAMI(19), No. 9, September 1997, pp. 989-1003.
IEEE DOI 9710
Find the nearest neighbor only if it is within some distance. Uses projections of the search space. BibRef

Cortijo, F.J., de la Blanca, N.P.[N. Perez],
The performance of regularized discriminant analysis versus non-parametric classifiers applied to high-dimensional image classification,
JRS(20), No. 17, November 1999, pp. 3345. BibRef 9911

Carr, J.R.[James R.], Matanawi, K.[Korblaah],
Correspondence Analysis for Principal Components Transformation of Multispectral and Hyperspectral Digital Images,
PhEngRS(65), No. 8, August 1999, pp. 909. captures 96% of the original image variance in first principal component. BibRef 9908

Benediktsson, J.A., Kanellopoulos, I.,
Classification of Multisource and Hyperspectral Data Based on Decision Fusion,
GeoRS(37), No. 3, May 1999, pp. 1367.
IEEE Top Reference. BibRef 9905

Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.,
Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles,
GeoRS(43), No. 3, March 2005, pp. 480-491.
IEEE Abstract. 0501
See also Multisource remote sensing data classification based on consensus and pruning. BibRef

Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.,
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles,
GeoRS(46), No. 11, November 2008, pp. 3804-3814.
IEEE DOI 0812
BibRef

Ghamisi, P., Benediktsson, J.A., Sveinsson, J.R.,
Automatic Spectral-Spatial Classification Framework Based on Attribute Profiles and Supervised Feature Extraction,
GeoRS(52), No. 9, September 2014, pp. 5771-5782.
IEEE DOI 1407
data reduction BibRef

Ghamisi, P., Dalla Mura, M., Benediktsson, J.A.,
A Survey on Spectral-Spatial Classification Techniques Based on Attribute Profiles,
GeoRS(53), No. 5, May 2015, pp. 2335-2353.
IEEE DOI 1502
geophysical image processing BibRef

Tarabalka, Y.[Yuliya], Benediktsson, J.A., Chanussot, J.[Jocelyn],
Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques,
GeoRS(47), No. 8, August 2009, pp. 2973-2987.
IEEE DOI 0907
BibRef

Tarabalka, Y.[Yuliya], Haavardsholm, T.V.[Trym Vegard], Kĺsen, I.[Ingebjřrg], Skauli, T.[Torbjřrn],
Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing,
RealTimeIP(4), No. 3, August 2009, pp. xx-yy.
Springer DOI 0909
BibRef

Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.,
Multiple Spectral-Spatial Classification Approach for Hyperspectral Data,
GeoRS(48), No. 11, November 2010, pp. 4122-4132.
IEEE DOI 1011
See also Segmentation and classification of hyperspectral images using watershed transformation. BibRef

Liu, T., Gu, Y., Jia, X., Benediktsson, J.A., Chanussot, J.,
Class-Specific Sparse Multiple Kernel Learning for Spectral-Spatial Hyperspectral Image Classification,
GeoRS(54), No. 12, December 2016, pp. 7351-7365.
IEEE DOI 1612
hyperspectral imaging See also Spectral-Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation. BibRef

Gu, Y., Chanussot, J., Jia, X., Benediktsson, J.A.,
Multiple Kernel Learning for Hyperspectral Image Classification: A Review,
GeoRS(55), No. 11, November 2017, pp. 6547-6565.
IEEE DOI 1711
Data structures, Kernel, Neural networks, Support vector machines, Classification, heterogeneous features, hyperspectral images (HSIs), multiple kernel learning (MKL), remote sensing BibRef

Wang, Q., Gu, Y., Tuia, D.,
Discriminative Multiple Kernel Learning for Hyperspectral Image Classification,
GeoRS(54), No. 7, July 2016, pp. 3912-3927.
IEEE DOI 1606
Hilbert space BibRef

Wang, Q.F.[Qi-Fan], Si, L.[Luo], Zhang, D.[Dan],
A Discriminative Data-Dependent Mixture-Model Approach for Multiple Instance Learning in Image Classification,
ECCV12(IV: 660-673).
Springer DOI 1210
BibRef

Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao], Duan, W.[Wuhui], Ren, J.C.[Jin-Chang], Benediktsson, J.A.,
Classification of Hyperspectral Images by Exploiting Spectral-Spatial Information of Superpixel via Multiple Kernels,
GeoRS(53), No. 12, December 2015, pp. 6663-6674.
IEEE DOI 1512
geophysical image processing BibRef

Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao], Kang, X.D.[Xu-Dong], Benediktsson, J.A.,
Spectral-Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation,
GeoRS(52), No. 12, December 2014, pp. 7738-7749.
IEEE DOI 1410
geophysical image processing See also Class-Specific Sparse Multiple Kernel Learning for Spectral-Spatial Hyperspectral Image Classification. BibRef

Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.,
Advances in Spectral-Spatial Classification of Hyperspectral Images,
PIEEE(100), No. 3, March 2013, pp. 652-675.
IEEE DOI 1303
BibRef

Fauvel, M.[Mathieu], Chanussot, J.[Jocelyn], Benediktsson, J.A.[Jon Atli],
A spatial-spectral kernel-based approach for the classification of remote-sensing images,
PR(45), No. 1, 2012, pp. 381-392.
Elsevier DOI 1410
Hyperspectral remote-sensing images BibRef

Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao], Kang, X.D.[Xu-Dong], Benediktsson, J.A.[Jon Atli],
Spectral-Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model,
GeoRS(53), No. 8, August 2015, pp. 4186-4201.
IEEE DOI 1506
geophysical image processing BibRef

Fauvel, M.[Mathieu], Chanussot, J.[Jocelyn], Benediktsson, J.A.[Jon Atli], Villa, A.,
Parsimonious Mahalanobis kernel for the classification of high dimensional data,
PR(46), No. 3, March 2013, pp. 845-854.
Elsevier DOI 1212
BibRef
Earlier: A1, A2, A3, Only:
Adaptive Pixel Neighborhood Definition for the Classification of Hyperspectral Images with Support Vector Machines and Composite Kernel,
ICIP08(1884-1887).
IEEE DOI 0810
SVM; High dimensional data; High dimensional discriminant analysis; Kernel methods; Hyperspectral imagery; Parsimonious Mahalanobis kernel BibRef

Fu, W., Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Benediktsson, J.A.[Jon Atli],
Adaptive Spectral-Spatial Compression of Hyperspectral Image With Sparse Representation,
GeoRS(55), No. 2, February 2017, pp. 671-682.
IEEE DOI 1702
geophysical image processing BibRef

Lu, T.[Ting], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Ma, Y.[Yi], Benediktsson, J.A.[Jon Atli],
Spectral-Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising,
GeoRS(54), No. 1, January 2016, pp. 373-385.
IEEE DOI 1601
geophysical image processing BibRef

Lu, T.[Ting], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Bruzzone, L., Benediktsson, J.A.[Jon Atli],
Set-to-Set Distance-Based Spectral-Spatial Classification of Hyperspectral Images,
GeoRS(54), No. 12, December 2016, pp. 7122-7134.
IEEE DOI 1612
geophysical image processing BibRef

Zhang, S.Z.[Shu-Zhen], Li, S.T.[Shu-Tao], Fu, W.[Wei], Fang, L.Y.[Lei-Yuan],
Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Dian, R.[Renwei], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan],
Non-local sparse representation for hyperspectral image super-resolution,
ICIP16(2832-2835)
IEEE DOI 1610
Databases BibRef

Yin, H.T.[Hai-Tao], Li, S.T.[Shu-Tao], Hu, J.W.[Jian-Wen],
Single image super resolution via texture constrained sparse representation,
ICIP11(1161-1164).
IEEE DOI 1201
BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Li, M.X.[Mei-Xiu], Benediktsson, J.A.,
Extended Random Walker-Based Classification of Hyperspectral Images,
GeoRS(53), No. 1, January 2015, pp. 144-153.
IEEE DOI 1410
geophysical image processing BibRef

Sun, B., Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Benediktsson, J.A.,
Random-Walker-Based Collaborative Learning for Hyperspectral Image Classification,
GeoRS(55), No. 1, January 2017, pp. 212-222.
IEEE DOI 1701
hyperspectral imaging BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Benediktsson, J.A.,
Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering,
GeoRS(52), No. 5, May 2014, pp. 2666-2677.
IEEE DOI 1403
See also Pansharpening With Matting Model. BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Benediktsson, J.A.,
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering,
GeoRS(52), No. 6, June 2014, pp. 3742-3752.
IEEE DOI 1403
Accuracy BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Benediktsson, J.A.,
Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images,
GeoRS(53), No. 4, April 2015, pp. 2241-2253.
IEEE DOI 1502
feature extraction BibRef

Li, J., Marpu, P.R., Plaza, A., Bioucas-Dias, J.M., Benediktsson, J.A.,
Generalized Composite Kernel Framework for Hyperspectral Image Classification,
GeoRS(51), No. 9, 2013, pp. 4816-4829.
IEEE DOI 1309
Educational institutions BibRef

Liu, J., Wu, Z., Li, J., Plaza, A., Yuan, Y.,
Probabilistic-Kernel Collaborative Representation for Spatial: Spectral Hyperspectral Image Classification,
GeoRS(54), No. 4, April 2016, pp. 2371-2384.
IEEE DOI 1604
Adaptation models BibRef

Yuan, Y., Lin, J., Wang, Q.,
Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization,
Cyber(46), No. 12, December 2016, pp. 2966-2977.
IEEE DOI 1612
Correlation BibRef

Liu, J., Lu, W.,
A Probabilistic Framework for Spectral-Spatial Classification of Hyperspectral Images,
GeoRS(54), No. 9, September 2016, pp. 5375-5384.
IEEE DOI 1609
estimation theory BibRef

Jimenez, L.O., Morales-Morell, A., Creus, A.,
Classification of Hyperdimensional Data Based on Feature and Decision Fusion Approaches Using Projection Pursuit, Majority Voting, and Neural Networks,
GeoRS(37), No. 3, May 1999, pp. 1360.
IEEE Top Reference. BibRef 9905

Ifarraguerri, A., Chang, C.I.,
Multispectral and Hyperspectral Image Analysis with Convex Cones,
GeoRS(37), No. 2, March 1999, pp. 756.
IEEE Top Reference. BibRef 9903

Ifarraguerri, A., Chang, C.I.[Chein-I],
Unsupervised Hyperspectral Image Analysis with Projection Pursuit,
GeoRS(38), No. 6, November 2000, pp. 2529-2538.
IEEE Top Reference. 0011
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Chang, C.I.[Chein-I],
Hyperspectral Imaging: Techniques for Spectral Detection and Classification,
Plenum2004. ISBN:0-306-47483-2.
HTML Version. BibRef 0400

Tu, T.M.[Te-Ming], Shyu, H.C.[Hsuen-Chyun], Lee, C.H.[Ching-Hai], Chang, C.I.[Chein-I],
An oblique subspace projection approach for mixed pixel classification in hyperspectral images,
PR(32), No. 8, August 1999, pp. 1399-1408.
WWW Link. See also Anomaly detection and classification for hyperspectral imagery. BibRef 9908

Pesses, M.E.,
Least-Squares-Filter Vector Hybrid Approach to Hyperspectral Subpixel Demixing,
GeoRS(37), No. 2, March 1999, pp. 846.
IEEE Top Reference. BibRef 9903

McGwire, K.[Kenneth], Minor, T.[Timothy], Fenstermaker, L.[Lynn],
Hyperspectral Mixture Modeling for Quantifying Sparse Vegetation Cover in Arid Environments,
RSE(72), No. 3, 2000, pp. 360-374. 0005
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Schweizer, S.M., Moura, J.M.F.,
Efficient detection in hyperspectral imagery,
IP(10), No. 4, April 2001, pp. 584-597.
IEEE DOI 0104
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Healey, G., Slater, D.A.,
Models and Methods for Automated Material Indentification in Hyperspectral Imagery Acquired under Unknown Illumination and Atmospheric Conditions,
GeoRS(37), No. 6, November 1999, pp. 2707-2717.
IEEE Top Reference. BibRef 9911

Suen, P., Healey, G., Slater, D.A.,
The impact of viewing geometry on material discriminability in hyperspectral images,
GeoRS(39), No. 7, July 2001, pp. 1352-1359.
IEEE Top Reference. 0108
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Kumar, S., Ghosh, J., Crawford, M.M.,
Best-bases feature extraction algorithms for classification of hyperspectral data,
GeoRS(39), No. 7, July 2001, pp. 1368-1379.
IEEE Top Reference. 0108
Generalized Local Discriminant Bases BibRef

Rajan, S., Ghosh, J., Crawford, M.M.,
Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data,
GeoRS(44), No. 11, November 2006, pp. 3408-3417.
IEEE DOI 0611
BibRef

Rajan, S., Ghosh, J., Crawford, M.M.,
An Active Learning Approach to Hyperspectral Data Classification,
GeoRS(46), No. 4, April 2008, pp. 1231-1242.
IEEE DOI 0803
BibRef

Kim, W., Crawford, M.M.,
Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines,
GeoRS(48), No. 11, November 2010, pp. 4110-4121.
IEEE DOI 1011
BibRef

Di, W., Crawford, M.M.,
View Generation for Multiview Maximum Disagreement Based Active Learning for Hyperspectral Image Classification,
GeoRS(50), No. 5, May 2012, pp. 1942-1954.
IEEE DOI 1202
BibRef

Funk, C.C., Theiler, J., Roberts, D.A., Borel, C.C.,
Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery,
GeoRS(39), No. 7, July 2001, pp. 1410-1420.
IEEE Top Reference. 0108
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Aiazzi, B., Alparone, L., Barducci, A., Baronti, S., Pippi, I.,
Information-theoretic assessment of sampled hyperspectral imagers,
GeoRS(39), No. 7, July 2001, pp. 1447-1458.
IEEE Top Reference. 0108
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Lewis, M., Jooste, V., de Gasparis, A.A.,
Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery,
GeoRS(39), No. 7, July 2001, pp. 1471-1479.
IEEE Top Reference. 0108
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Tsai, F.[Fuan], Philpot, W.D.,
A derivative-aided hyperspectral image analysis system for land-cover classification,
GeoRS(40), No. 2, February 2002, pp. 416-425.
IEEE Top Reference. 0205
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Thai, B.[Bea], Healey, G.[Glenn],
Invariant subpixel material detection in hyperspectral imagery,
GeoRS(40), No. 3, March 2002, pp. 599-608.
IEEE Top Reference. 0206
BibRef
And:
Invariant Subpixel Material Identification in Hyperspectral Imagery,
DARPA98(809-814). BibRef
Earlier:
Using a Linear Subspace Approach for Invariant Subpixel Material Identification in Airborne Hyperspectral Imagery,
CVPR99(I: 567-572).
IEEE DOI BibRef

Jia, X.P.[Xiu-Ping], Richards, J.A.,
Cluster-space representation for hyperspectral data classification,
GeoRS(40), No. 3, March 2002, pp. 593-598.
IEEE Top Reference. 0206
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Jia, X.P.[Xiu-Ping], Richards, J.A.,
Efficient transmission and classification of hyperspectral image data,
GeoRS(41), No. 5, May 2003, pp. 1129-1131.
IEEE Abstract. 0307
BibRef

Holden, H.[Heather], LeDrew, E.[Ellsworth],
Measuring and modeling water column effects on hyperspectral reflectance in a coral reef environment,
RSE(81), No. 2-3, August 2002, pp. 300-308.
HTML Version. 0206
BibRef

Bakker, W.H., Schmidt, K.S.,
Hyperspectral edge filtering for measuring homogeneity of surface cover types,
PandRS(56), No. 4, July 2002, pp. 246-256.
HTML Version. 0207
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Priebe, C.E.[Carey E.], Marchette, D.J.[David J.],
Adaptive mixtures: Recursive nonparametric pattern recognition,
PR(24), No. 12, 1991, pp. 1197-1209.
WWW Link. 0401
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And: Futher Analysis of method:
Adaptive mixture density estimation,
PR(26), No. 5, May 1993, pp. 771-785.
WWW Link. 0401
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Marchette, D.J.[David J.], Priebe, C.E.[Carey E.],
Characterizing the scale dimension of a high-dimensional classification problem,
PR(36), No. 1, January 2003, pp. 45-60.
WWW Link. 0210
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Staenz, K., Secker, J., Gao, B.C., Davis, C., Nadeau, C.,
Radiative transfer codes applied to hyperspectral data for the retrieval of surface reflectance,
PandRS(57), No. 3, December 2002, pp. 194-203.
WWW Link. 0307
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Li, R.R., Lucke, R., Korwan, D., Gao, B.C.,
A Technique For Removing Second-Order Light Effects From Hyperspectral Imaging Data,
GeoRS(50), No. 3, March 2012, pp. 824-830.
IEEE DOI 1203
BibRef

Chen, W., Lucke, R.,
Out-of-Band Correction for Multispectral Remote Sensing,
GeoRS(51), No. 4, April 2013, pp. 2476-2483.
IEEE DOI 1304
BibRef

Verhoef, W.[Wout], Bach, H.[Heike],
Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models,
RSE(87), No. 1, 15 September 2003, pp. 23-41.
WWW Link. 0309
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Guo, D.[Diansheng], Peuquet, D.J.[Donna J.], Gahegan, M.[Mark],
ICEAGE: Interactive Clustering and Exploration of Large and High-Dimensional Geodata,
GeoInfo(7), No. 3, September 2003, pp. 229-253.
DOI Link 0309
BibRef

Bachmann, C.M.,
Improving the performance of classifiers in high-dimensional remote sensing applications: an adaptive resampling strategy for error-prone exemplars (ARESEPE),
GeoRS(41), No. 9, September 2003, pp. 2101-2112.
IEEE Abstract. 0310
BibRef

Paclík, P.[Pavel], Duin, R.P.W.[Robert P. W.],
Dissimilarity-based classification of spectra: computational issues,
RealTimeImg(9), No. 4, August 2003, pp. 237-244.
WWW Link.
PDF File. 0311
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Bioucas-Dias, J.M., Nascimento, J.M.P.,
Hyperspectral Subspace Identification,
GeoRS(46), No. 8, August 2008, pp. 2435-2445.
IEEE DOI 0808
See also Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data. BibRef

Borges, J.S.[Janete S.], Bioucas-Dias, J.M.B.[José M.B.], Marçal, A.R.S.[André R.S.],
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GeoRS(49), No. 6, June 2011, pp. 2151-2164.
IEEE DOI 1106
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Earlier: IbPRIA07(I: 22-29).
Springer DOI 0706
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Bachmann, C.M., Ainsworth, T.L., Fusina, R.A.,
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GeoRS(43), No. 3, March 2005, pp. 441-454.
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Camps-Valls, G., Bruzzone, L.,
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GeoRS(43), No. 6, June 2005, pp. 1351-1362.
IEEE Abstract. 0506
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Camps-Valls, G., Bandos Marsheva, T.V., Zhou, D.,
Semi-Supervised Graph-Based Hyperspectral Image Classification,
GeoRS(45), No. 10, October 2007, pp. 3044-3054.
IEEE DOI 0711
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Capobianco, L., Garzelli, A., Camps-Valls, G.,
Target Detection With Semisupervised Kernel Orthogonal Subspace Projection,
GeoRS(47), No. 11, November 2009, pp. 3822-3833.
IEEE DOI 0911
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Ratle, F., Camps-Valls, G., Weston, J.,
Semisupervised Neural Networks for Efficient Hyperspectral Image Classification,
GeoRS(48), No. 5, May 2010, pp. 2271-2282.
IEEE DOI 1006
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Neher, R., Srivastava, A.,
A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging,
GeoRS(43), No. 6, June 2005, pp. 1363-1374.
IEEE Abstract. 0506
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Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., Ramon, H.,
Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps,
RealTimeImg(11), No. 2, April 2005, pp. 75-83.
WWW Link. 0506
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Tatzer, P.[Petra], Wolf, M.[Markus], Panner, T.[Thomas],
Industrial application for inline material sorting using hyperspectral imaging in the NIR range,
RealTimeImg(11), No. 2, April 2005, pp. 99-107.
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Pilevar, A.H., Sukumar, M.,
GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases,
PRL(26), No. 7, 15 May 2005, pp. 999-1010.
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Purkis, S.J.,
A 'Reef-Up' Approach to Classifying Coral Habitats From IKONOS Imagery,
GeoRS(43), No. 6, June 2005, pp. 1375-1390.
IEEE Abstract. 0506
Using hyperspectral data, calibrate based on field measurements of reflectance. BibRef

Othman, H., Qian, S.E.,
Noise Reduction of Hyperspectral Imagery Using Hybrid Spatial-Spectral Derivative-Domain Wavelet Shrinkage,
GeoRS(44), No. 2, February 2006, pp. 397-408.
IEEE DOI 0602
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Brown, A.J.,
Spectral Curve Fitting for Automatic Hyperspectral Data Analysis,
GeoRS(44), No. 6, June 2006, pp. 1601-1608.
IEEE DOI 0606
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Weinberger, K.Q.[Kilian Q.], Saul, L.K.[Lawrence K.],
Unsupervised Learning of Image Manifolds by Semidefinite Programming,
IJCV(70), No. 1, October 2006, pp. 77-90.
Springer DOI 0606
BibRef
Earlier: CVPR04(II: 988-995).
IEEE DOI 0408
Analyze high dimensional data. BibRef

Renzullo, L.J., Blanchfield, A.L., Powell, K.S.,
A Method of Wavelength Selection and Spectral Discrimination of Hyperspectral Reflectance Spectrometry,
GeoRS(44), No. 7, Part 2, July 2006, pp. 1986-1994.
IEEE DOI 0606
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Berge, A.[Asbjřrn], Solberg, A.S.[Anne Schistad],
Structured Gaussian Components for Hyperspectral Image Classification,
GeoRS(44), No. 11, November 2006, pp. 3386-3396.
IEEE DOI 0611
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Berge, A.[Asbjrn], Jensen, A.C.[Are C.], Solberg, A.H.S.[Anne H. Schistad],
Sparse Inverse Covariance Estimates for Hyperspectral Image Classification,
GeoRS(45), No. 5, May 2007, pp. 1399-1407.
IEEE DOI 0704
BibRef
Earlier: A1, A3, Only:
Sparse Covariance Estimates for High Dimensional Classification Using the Cholesky Decomposition,
SSPR06(835-843).
Springer DOI 0608
BibRef

Jensen, A.C.[Are C.], Berge, A.[Asbjrn], Solberg, A.H.S.[Anne H. Schistad],
Regression Approaches to Small Sample Inverse Covariance Matrix Estimation for Hyperspectral Image Classification,
GeoRS(46), No. 10, October 2008, pp. 2814-2822.
IEEE DOI 0810
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Jensen, A.C., Loog, M., Solberg, A.H.S.,
Using Multiscale Spectra in Regularizing Covariance Matrices for Hyperspectral Image Classification,
GeoRS(48), No. 4, April 2010, pp. 1851-1859.
IEEE DOI 1003
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Jensen, A.C.[Are Charles], Loog, M.[Marco],
Forming Different-Complexity Covariance-Model Subspaces through Piecewise-Constant Spectra for Hyperspectral Image Classification,
SCIA11(186-195).
Springer DOI 1105
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Santurri, L.[Leonardo],
Aliasing assessment in wavelength domain of hyperspectral data,
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Rud, R.[Ronit], Shoshany, M.[Maxim], Alchanatis, V.[Victor], Cohen, Y.[Yafit],
Application of spectral features' ratios for improving classification in partially calibrated hyperspectral imagery: a case study of separating Mediterranean vegetation species,
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Springer DOI 0001
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Kogan, J.[Jacob],
Introduction to Clustering Large and High-Dimensional Data,
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Hsu, P.H.[Pai-Hui],
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PandRS(62), No. 2, June 2007, pp. 78-92.
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Kasapoglu, N.G., Ersoy, O.K.,
Border Vector Detection and Adaptation for Classification of Multispectral and Hyperspectral Remote Sensing Images,
GeoRS(45), No. 12, December 2007, pp. 3880-3893.
IEEE DOI 0711
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Kaya, G.T.[G. Taskin], Ersoy, O.K., Kamasak, M.E.,
Support Vector Selection and Adaptation for Remote Sensing Classification,
GeoRS(49), No. 6, June 2011, pp. 2071-2079.
IEEE DOI 1106
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Bali, N., Mohammad-Djafari, A.,
Bayesian Approach With Hidden Markov Modeling and Mean Field Approximation for Hyperspectral Data Analysis,
IP(17), No. 2, February 2008, pp. 217-225.
IEEE DOI 0801
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Bali, N., Mohammad-Djafari, A., Mohammadpoor, A.,
Joint Dimensionality Reduction, Classification and Segmentation of Hyperspectral Images,
ICIP06(969-972).
IEEE DOI 0610
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Guo, B.F.[Bao-Feng], Gunn, S.R.[Steve R.], Damper, R.I., Nelson, J.D.B.,
Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification,
IP(17), No. 4, April 2008, pp. 622-629.
IEEE DOI 0803
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Prasad, S., Bruce, L.M.,
Decision Fusion With Confidence-Based Weight Assignment for Hyperspectral Target Recognition,
GeoRS(46), No. 5, May 2008, pp. 1448-1456.
IEEE DOI 0804
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Orlov, N.[Nikita], Shamir, L.[Lior], Macura, T.[Tomasz], Johnston, J.[Josiah], Eckley, D.M.[D. Mark], Goldberg, I.G.[Ilya G.],
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Image classification; Biological imaging; Image features; High dimensional classification BibRef

Chang, C.I.[Chein-I], Chakravarty, S.[Sumit], Chen, H.M.[Hsian-Min], Ouyang, Y.C.[Yen-Chieh],
Spectral derivative feature coding for hyperspectral signature analysis,
PR(42), No. 3, March 2009, pp. 395-408.
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Spectral analysis manager (SPAM); Spectral derivative feature coding (SDFC); Spectral feature-based binary coding (SFBC) BibRef

Wu, C.C.[Chao-Cheng], Chen, H.M.[Hsian-Min], Chang, C.I.[Chein-I],
Real-time N-finder processing algorithms for hyperspectral imagery,
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Qiu, F.[Fang],
Neuro-fuzzy Based Analysis of Hyperspectral Imagery,
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A neuro-fuzzy system, namely Gaussian Fuzzy Learning Vector Quantization, was developed to efficiently and effectively analyze hyperspectral data. BibRef

Eddy, P.R., Smith, A.M., Hill, B.D., Peddle, D.R., Coburn, C.A., Blackshaw, R.E.,
Hybrid Segmentation: Artificial Neural Network Classification of High Resolution Hyperspectral Imagery for Site-Specific Herbicide Management in Agriculture,
PhEngRS(74), No. 10, October 2008, pp. 1249-1258.
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A new, efficient AI method is presented for improved weed management in crops with significant economic and environmental advantages. BibRef

Zhang, Q.A.[Qi-Ang], Wang, H.[Han], Plemmons, R.J.[Robert J.], Pauca, V.P.[V. Paul],
Tensor methods for hyperspectral data analysis: A space object material identification study,
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Liu, X.W.[Xiu-Wen], Zhang, Q.A.[Qi-Ang],
Spectral histogram representations for visual modeling,
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IEEE DOI 0310
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Chen, J., Jia, X., Yang, W., Matsushita, B.,
Generalization of Subpixel Analysis for Hyperspectral Data With Flexibility in Spectral Similarity Measures,
GeoRS(47), No. 7, July 2009, pp. 2165-2171.
IEEE DOI 0906
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Bellucci, J.P., Smetek, T.E., Bauer, K.W.,
Improved Hyperspectral Image Processing Algorithm Testing Using Synthetic Imagery and Factorial Designed Experiments,
GeoRS(48), No. 3, March 2010, pp. 1211-1223.
IEEE DOI 1003
BibRef

Kalluri, H.R., Prasad, S., Bruce, L.M.,
Decision-Level Fusion of Spectral Reflectance and Derivative Information for Robust Hyperspectral Land Cover Classification,
GeoRS(48), No. 11, November 2010, pp. 4047-4058.
IEEE DOI 1011
BibRef

Bue, B.D., Merenyi, E., Csatho, B.,
Automated Labeling of Materials in Hyperspectral Imagery,
GeoRS(48), No. 11, November 2010, pp. 4059-4070.
IEEE DOI 1011
BibRef

Li, J., Bioucas-Dias, J.M., Plaza, A.,
Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning,
GeoRS(48), No. 11, November 2010, pp. 4085-4098.
IEEE DOI 1011
BibRef

Li, J.[Jun], Bioucas-Dias, J.M., Plaza, A.,
Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning,
GeoRS(49), No. 10, October 2011, pp. 3947-3960.
IEEE DOI 1110
BibRef

Li, J., Bioucas-Dias, J.M., Plaza, A.,
Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning,
GeoRS(51), No. 2, February 2013, pp. 844-856.
IEEE DOI 1302
BibRef

Li, J., Bioucas-Dias, J.M., Plaza, A.,
Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields,
GeoRS(50), No. 3, March 2012, pp. 809-823.
IEEE DOI 1203
See also Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing. BibRef

Samat, A.[Alim], Li, J.[Jun], Liu, S.[Sicong], Du, P.J.[Pei-Jun], Miao, Z.L.[Ze-Lang], Luo, J.Q.[Jie-Qiong],
Improved hyperspectral image classification by active learning using pre-designed mixed pixels,
PR(51), No. 1, 2016, pp. 43-58.
Elsevier DOI 1601
Sample design BibRef

Li, J.[Jun], Plaza, A., Bioucas-Dias, J.M.,
Integration of Hyperspectral Image Classification and Unmixing for Active Learning,
ISIDF11(1-4).
IEEE DOI 1111
BibRef

Cao, G., Bachega, L.R., Bouman, C.A.,
The Sparse Matrix Transform for Covariance Estimation and Analysis of High Dimensional Signals,
IP(20), No. 3, March 2011, pp. 625-640.
IEEE DOI 1103
BibRef

Moudden, Y., Bobin, J.,
Hyperspectral BSS Using GMCA With Spatio-Spectral Sparsity Constraints,
IP(20), No. 3, March 2011, pp. 872-879.
IEEE DOI 1103
BibRef

Plaza, A.[Antonio], Plaza, J.[Javier], Paz, A., Sanchez, S.,
Parallel Hyperspectral Image and Signal Processing,
SPMag(28), No. 3, 2011, pp. 119-126.
IEEE DOI 1105
Applications Corner BibRef

Mianji, F.A.[Fereidoun A.], Zhang, Y.[Ye],
Robust Hyperspectral Classification Using Relevance Vector Machine,
GeoRS(49), No. 6, June 2011, pp. 2100-2112.
IEEE DOI 1106
BibRef
Earlier:
Improved hyperspectral land-cover analysis using relevance vector machine,
ICIP10(2281-2284).
IEEE DOI 1009
BibRef

McGwire, K.C., Minor, T.B., Schultz, B.W.,
Progressive Discrimination: An Automatic Method for Mapping Individual Targets in Hyperspectral Imagery,
GeoRS(49), No. 7, July 2011, pp. 2674-2685.
IEEE DOI 1107
BibRef

Chen, Y.[Yi], Nasrabadi, N.M.[Nasser M.], Tran, T.D.[Trac D.],
Hyperspectral Image Classification Using Dictionary-Based Sparse Representation,
GeoRS(49), No. 10, October 2011, pp. 3973-3985.
IEEE DOI 1110
BibRef

Chen, Y.[Yi], Nasrabadi, N.M.[Nasser M.], Tran, T.D.[Trac D.],
Hyperspectral Image Classification via Kernel Sparse Representation,
GeoRS(51), No. 1, January 2013, pp. 217-231.
IEEE DOI 1301
BibRef
And: ICIP11(1233-1236).
IEEE DOI 1201
BibRef

Greer, J.B.,
Sparse Demixing of Hyperspectral Images,
IP(21), No. 1, January 2012, pp. 219-228.
IEEE DOI 1112
BibRef

Gonzalez, C., Mozos, D., Resano, J., Plaza, A.,
FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis,
GeoRS(50), No. 2, February 2012, pp. 374-388.
IEEE DOI 1201
BibRef

Shen, L.L.[Lin-Lin], Jia, S.[Sen],
Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification,
GeoRS(49), No. 12, December 2011, pp. 5039-5046.
IEEE DOI 1201
BibRef

Jia, S.[Sen], Shen, L.L.[Lin-Lin], Li, Q.Q.[Qing-Quan],
Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification,
GeoRS(53), No. 2, February 2015, pp. 1118-1129.
IEEE DOI 1411
compressed sensing BibRef

Jia, S.[Sen], Hu, J.[Jie], Zhu, J.S.[Jia-Song], Jia, X.P.[Xiu-Ping], Li, Q.Q.[Qing-Quan],
Three-Dimensional Local Binary Patterns for Hyperspectral Imagery Classification,
GeoRS(55), No. 4, April 2017, pp. 2399-2413.
IEEE DOI 1704
feature extraction BibRef

Jia, S.[Sen], Hu, J., Xie, Y., Shen, L.L.[Lin-Lin], Jia, X.P.[Xiu-Ping], Li, Q.Q.[Qing-Quan],
Gabor Cube Selection Based Multitask Joint Sparse Representation for Hyperspectral Image Classification,
GeoRS(54), No. 6, June 2016, pp. 3174-3187.
IEEE DOI 1606
Gabor filters BibRef

Jia, S.[Sen], Deng, B.[Bin], Zhu, J.S.[Jia-Song], Jia, X.P.[Xiu-Ping], Li, Q.Q.[Qing-Quan],
Superpixel-Based Multitask Learning Framework for Hyperspectral Image Classification,
GeoRS(55), No. 5, May 2017, pp. 2575-2588.
IEEE DOI 1705
filtering theory, hyperspectral imaging, remote sensing by radar, Gabor filters, classification performance, computational complexity, hyperspectral image classification, pixel-based spatial-spectral Schroedinger eigenmaps method, sufficient labeled samples, superpixel-based multitask learning framework, Computational complexity, Data mining, Feature extraction, Hyperspectral imaging, Support vector machines, Training, Dimensionality reduction, hyperspectral image classification, multitask learning, superpixel BibRef

Li, S.T.[Shu-Tao], Lu, T.[Ting], Fang, L.Y.[Le-Yuan], Jia, X.P.[Xiu-Ping], Benediktsson, J.A.[Jón Atli],
Probabilistic Fusion of Pixel-Level and Superpixel-Level Hyperspectral Image Classification,
GeoRS(54), No. 12, December 2016, pp. 7416-7430.
IEEE DOI 1612
geophysical image processing BibRef

Lu, T.[Ting], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Jia, X.P.[Xiu-Ping], Benediktsson, J.A.[Jón Atli],
From Subpixel to Superpixel: A Novel Fusion Framework for Hyperspectral Image Classification,
GeoRS(55), No. 8, August 2017, pp. 4398-4411.
IEEE DOI 1708
Data mining, Feature extraction, Geometry, Hyperspectral imaging, Probabilistic logic, Support vector machines, Decision fusion, feature fusion, hyperspectral image (HSI) classification, pixel, subpixel, superpixel BibRef

Blanes, I., Serra-Sagrista, J., Marcellin, M.W., Bartrina-Rapesta, J.,
Divide-and-Conquer Strategies for Hyperspectral Image Processing: A Review of Their Benefits and Advantages,
SPMag(29), No. 3, 2012, pp. 71-81.
IEEE DOI 1204
BibRef

Bajorski, P.,
Generalized Detection Fusion for Hyperspectral Images,
GeoRS(50), No. 4, April 2012, pp. 1199-1205.
IEEE DOI 1204
BibRef

Sami ul Haq, Q., Tao, L., Sun, F., Yang, S.,
A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples,
GeoRS(50), No. 6, June 2012, pp. 2287-2302.
IEEE DOI 1205
BibRef

Roscher, R., Waske, B., Forstner, W.,
Incremental Import Vector Machines for Classifying Hyperspectral Data,
GeoRS(50), No. 9, September 2012, pp. 3463-3473.
IEEE DOI 1209
BibRef

Velasco-Forero, S.[Santiago], Angulo, J.[Jesus],
Classification of hyperspectral images by tensor modeling and additive morphological decomposition,
PR(46), No. 2, February 2013, pp. 566-577.
Elsevier DOI 1210
Hyperspectral images; Mathematical morphology; Pixelwise classification; Tensor modeling BibRef

Gholizadeh, H.[Hamed], Valadan Zoej, M.J.[Mohammad Javad], Mojaradi, B.[Barat],
A Decision Fusion Framework for Hyperspectral Subpixel Target Detection,
PFG(2012), No. 3, 2012, pp. 267-280.
WWW Link. 1211
BibRef

Schmidt, K.[Kai],
Analyse hyperspektraler Signaturen mit doppelten Weibull-Funktionen,
PFG(2011), No. 5, 2011, pp. 349-359.
WWW Link. 1211
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Forzieri, G.[Giovanni], Moser, G.[Gabriele], Catani, F.[Filippo],
Assessment of hyperspectral MIVIS sensor capability for heterogeneous landscape classification,
PandRS(74), No. 1, November 2012, pp. 175-184.
Elsevier DOI 1212
Hyperspectral; MIVIS; Classification; Feature reduction; Complex land covers/uses BibRef

Bai, J., Xiang, S., Pan, C.,
A Graph-Based Classification Method for Hyperspectral Images,
GeoRS(51), No. 2, February 2013, pp. 803-817.
IEEE DOI 1302
BibRef

Lunga, D.[Dalton], Ersoy, O.[Okan],
Spherical Stochastic Neighbor Embedding of Hyperspectral Data,
GeoRS(51), No. 2, February 2013, pp. 857-871.
IEEE DOI 1302
BibRef
Earlier:
Kent mixture model for classification of remote sensing data on spherical manifolds,
AIPR11(1-7).
IEEE DOI 1204
BibRef

Gurram, P., Kwon, H.,
Sparse Kernel-Based Ensemble Learning With Fully Optimized Kernel Parameters for Hyperspectral Classification Problems,
GeoRS(51), No. 2, February 2013, pp. 787-802.
IEEE DOI 1302
Cited by 1 BibRef

Veganzones, M.A., Tochon, G.[Guillaume], Dalla-Mura, M.[Mauro], Plaza, A.J., Chanussot, J.[Jocelyn],
Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation,
IP(23), No. 8, August 2014, pp. 3574-3589.
IEEE DOI 1408
BibRef
Earlier:
Hyperspectral Image Segmentation Using a New Spectral Mixture-Based Binary Partition Tree Representation,
ICIP13(245-249)
IEEE DOI 1402
geophysical image processing Hyperspectral imaging See also Hyperspectral Image Representation and Processing With Binary Partition Trees. BibRef

Tochon, G.[Guillaume], Dalla-Mura, M.[Mauro], Chanussot, J.[Jocelyn],
Segmentation of Multimodal Images Based on Hierarchies of Partitions,
ISMM15(241-252).
Springer DOI 1506
See also Context-Adaptive Pansharpening Based on Binary Partition Tree Segmentation. BibRef

Lopez, S., Vladimirova, T., Gonzalez, C., Resano, J., Mozos, D., Plaza, A.,
The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends,
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IEEE DOI 1303
BibRef

Cerra, D., Mueller, R., Reinartz, P.,
A Classification Algorithm for Hyperspectral Images Based on Synergetics Theory,
GeoRS(51), No. 5, May 2013, pp. 2887-2898.
IEEE DOI 1305
BibRef
Earlier:
A Classification Algorithm for Hyperspectral Data Based On Synergetics Theory,
AnnalsPRS(I-7), No. 2012, pp. 71-76.
HTML Version. 1209
BibRef

Cerra, D., Müller, R., Reinartz, P.,
About the Applications of Unmixing-Based Denoising for Hyperspectral Data,
SMPR13(103-106).
HTML Version. 1311
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Müller, R., Cerra, D., Reinartz, P.,
Synergetics Framework for Hyperspectral Image Classification,
Hannover13(257-262).
DOI Link 1308
See also Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion. BibRef

Makarau, A., Müller, R., Palubinskas, G., Reinartz, P.,
Hyperspectral Data Classification Using Factor Graphs,
ISPRS12(XXXIX-B7:137-140).
DOI Link 1209
BibRef

Tong, X., Zhang, X., Shan, J., Xie, H., Liu, M.,
Attraction-Repulsion Model-Based Subpixel Mapping of Multi-/Hyperspectral Imagery,
GeoRS(51), No. 5, May 2013, pp. 2799-2814.
IEEE DOI 1305
BibRef

Lin, T.[Tao], Bourennane, S.,
Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor,
GeoRS(51), No. 6, 2013, pp. 3529-3541.
IEEE DOI 1307
hyperspectral imaging; wavelet packet transform BibRef

Rajwade, A.[Ajit], Kittle, D.[David], Tsai, T.H.[Tsung-Han], Brady, D.[David], Carin, L.[Lawrence],
Coded Hyperspectral Imaging and Blind Compressive Sensing,
SIIMS(6), No. 2, 2013, pp. 782-812.
DOI Link 1307
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Howle, C.[Christopher], Clewes, R.[Rhea], Guicheteau, J.[Jason], Ruxton, K.[Keith], Malcolm, G.[Graeme],
Imager locates toxic liquids at stand-off distances,
SPIE(Newsroom), May 15, 2013
DOI Link 1308
A novel hyperspectral imaging system can locate liquid chemical warfare agents at stand-off distances, improving operator safety and enabling the rapid survey of scenes. BibRef

Golbabaee, M., Arberet, S., Vandergheynst, P.,
Compressive Source Separation: Theory and Methods for Hyperspectral Imaging,
IP(22), No. 12, 2013, pp. 5096-5110.
IEEE DOI 1312
compressed sensing BibRef

Salas, E.A.L.[Eric Ariel L.], Henebry, G.M.[Geoffrey M.],
A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method,
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Lunga, D., Ersoy, O.,
Multidimensional Artificial Field Embedding With Spatial Sensitivity,
GeoRS(52), No. 2, February 2014, pp. 1518-1532.
IEEE DOI 1402
embedded systems spectral signature relations in hyperspectral images. BibRef

Wang, Z.Y.[Zhang-Yang], Nasrabadi, N.M., Huang, T.S.,
Spatial-Spectral Classification of Hyperspectral Images Using Discriminative Dictionary Designed by Learning Vector Quantization,
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IEEE DOI 1403
Bayes methods BibRef

Wang, Z.Y.[Zhang-Yang], Nasrabadi, N.M.[Nasser M.], Huang, T.S.,
Semisupervised Hyperspectral Classification Using Task-Driven Dictionary Learning With Laplacian Regularization,
GeoRS(53), No. 3, March 2015, pp. 1161-1173.
IEEE DOI 1412
geophysical image processing BibRef

Sun, X.X.[Xiao-Xia], Nasrabadi, N.M.[Nasser M.], Tran, T.D.[Trac D.],
Task-Driven Dictionary Learning for Hyperspectral Image Classification With Structured Sparsity Constraints,
GeoRS(53), No. 8, August 2015, pp. 4457-4471.
IEEE DOI 1506
BibRef
Earlier: ICIP14(5262-5266)
IEEE DOI 1502
hyperspectral imaging. Dictionaries BibRef

He, Z., Wang, Q., Shen, Y., Sun, M.,
Kernel Sparse Multitask Learning for Hyperspectral Image Classification With Empirical Mode Decomposition and Morphological Wavelet-Based Features,
GeoRS(52), No. 8, August 2014, pp. 5150-5163.
IEEE DOI 1403
Feature extraction BibRef

Ghamisi, P., Benediktsson, J.A., Ulfarsson, M.O.,
Spectral-Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields,
GeoRS(52), No. 5, May 2014, pp. 2565-2574.
IEEE DOI 1403
Hidden Markov random field (HMRF) BibRef

Yu, H.Y.[Hao-Yang], Gao, L.R.[Lian-Ru], Li, J.[Jun], Li, S.S.[Shan Shan], Zhang, B.[Bing], Benediktsson, J.A.[Jón Atli],
Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields,
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Li, L.[Liwei], Zhang, B.[Bing], Li, W.[Wei], Gao, L.R.[Lian-Ru],
Orthogonal polynomial function fitting for hyperspectral data representation and discrimination,
PRL(83, Part 2), No. 1, 2016, pp. 160-168.
Elsevier DOI 1609
Orthogonal polynomial function BibRef

Kuester, T., Spengler, D., Barczi, J.F., Segl, K., Hostert, P., Kaufmann, H.,
Simulation of Multitemporal and Hyperspectral Vegetation Canopy Bidirectional Reflectance Using Detailed Virtual 3-D Canopy Models,
GeoRS(52), No. 4, April 2014, pp. 2096-2108.
IEEE DOI 1403
crops BibRef

Ji, R.R.[Rong-Rong], Gao, Y.[Yue], Hong, R.[Richang], Liu, Q.[Qiong], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Spectral-Spatial Constraint Hyperspectral Image Classification,
GeoRS(52), No. 3, March 2014, pp. 1811-1824.
IEEE DOI 1403
hyperspectral imaging BibRef

Willett, R.M., Duarte, M.F., Davenport, M.A., Baraniuk, R.G.,
Sparsity and Structure in Hyperspectral Imaging: Sensing, Reconstruction, and Target Detection,
SPMag(31), No. 1, January 2014, pp. 116-126.
IEEE DOI 1403
geophysical image processing BibRef

Camps-Valls, G., Tuia, D., Bruzzone, L., Benediktsson, J.A.[J. Atli],
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods,
SPMag(31), No. 1, January 2014, pp. 45-54.
IEEE DOI 1403
computer vision BibRef

Manolakis, D., Truslow, E., Pieper, M., Cooley, T., Brueggeman, M.,
Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms,
SPMag(31), No. 1, January 2014, pp. 24-33.
IEEE DOI 1403
Survey, Object Detection. hyperspectral imaging BibRef

Gao, Y., Ji, R.R., Cui, P., Dai, Q.H., Hua, G.,
Hyperspectral Image Classification Through Bilayer Graph-Based Learning,
IP(23), No. 7, July 2014, pp. 2769-2778.
IEEE DOI 1407
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Gao, Y.[Yue], Ji, R.R.[Rong-Rong], Liu, W.[Wei], Dai, Q.H.[Qiong-Hai], Hua, G.[Gang],
Weakly Supervised Visual Dictionary Learning by Harnessing Image Attributes,
IP(23), No. 12, December 2014, pp. 5400-5411.
IEEE DOI 1412
dictionaries BibRef

Chen, C.[Chen], Li, W.[Wei], Su, H.J.[Hong-Jun], Liu, K.[Kui],
Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine,
RS(6), No. 6, 2014, pp. 5795-5814.
DOI Link 1407
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Li, W.[Wei], Chen, C.[Chen], Su, H.J.[Hong-Jun], Du, Q.,
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification,
GeoRS(53), No. 7, July 2015, pp. 3681-3693.
IEEE DOI 1503
Educational institutions BibRef

Li, W.[Wei], Wu, G.D.[Guo-Dong], Zhang, F.[Fan], Du, Q.[Qian],
Hyperspectral Image Classification Using Deep Pixel-Pair Features,
GeoRS(55), No. 2, February 2017, pp. 844-853.
IEEE DOI 1702
hyperspectral imaging BibRef

Li, H.[Hong], Xiao, G.R.[Guang-Run], Xia, T.[Tian], Tang, Y.Y., Li, L.Q.[Luo-Qing],
Hyperspectral Image Classification Using Functional Data Analysis,
Cyber(44), No. 9, September 2014, pp. 1544-1555.
IEEE DOI 1410
hyperspectral imaging BibRef

Zhong, Y.F.[Yan-Fei], Wu, Y.Y.[Yun-Yun], Zhang, L.P.[Liang-Pei], Xu, X.[Xiong],
Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery,
PandRS(96), No. 1, 2014, pp. 134-148.
Elsevier DOI 1410
Hyperspectral image BibRef

Acito, N.[Nicola], Diani, M.[Marco],
Mitigating the impact of signal-dependent noise on hyperspectral target detection,
SPIE(Newsroom), September 18, 2014.
DOI Link 1410
Noise Removal. A pre-processing procedure can diminish the data noise from new-generation hyperspectral sensors, thus minimizing negative impacts on target detection algorithms. BibRef

Li, J.[Jun], Huang, X.[Xin], Gamba, P., Bioucas-Dias, J.M., Zhang, L.P.[Liang-Pei], Atli Benediktsson, J., Plaza, A.,
Multiple Feature Learning for Hyperspectral Image Classification,
GeoRS(53), No. 3, March 2015, pp. 1592-1606.
IEEE DOI 1412
feature extraction BibRef

Wan, L.J.[Lun-Jun], Tang, K.[Ke], Li, M.Z.[Ming-Zhi], Zhong, Y.F.[Yan-Fei], Qin, A.K.,
Collaborative Active and Semisupervised Learning for Hyperspectral Remote Sensing Image Classification,
GeoRS(53), No. 5, May 2015, pp. 2384-2396.
IEEE DOI 1502
geophysical image processing BibRef

Xia, J.S.[Jun-Shi], Chanussot, J., Du, P.J.[Pei-Jun], He, X.[Xiyan],
Spectral-Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields,
GeoRS(53), No. 5, May 2015, pp. 2532-2546.
IEEE DOI 1502
Markov processes BibRef

Xia, J.S.[Jun-Shi], Chanussot, J., Du, P.J.[Pei-Jun], He, X.[Xiyan],
Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples,
GeoRS(54), No. 3, March 2016, pp. 1519-1531.
IEEE DOI 1603
Accuracy BibRef

Xia, J.S.[Jun-Shi], Mura, M.D.[M. Dalla], Chanussot, J., Du, P.J.[Pei-Jun], He, X.[Xiyan],
Random Subspace Ensembles for Hyperspectral Image Classification With Extended Morphological Attribute Profiles,
GeoRS(53), No. 9, September 2015, pp. 4768-4786.
IEEE DOI 1506
Feature extraction BibRef

Aptoula, E., Mura, M.D.[M. Dalla], LefŽčvre, S.,
Vector Attribute Profiles for Hyperspectral Image Classification,
GeoRS(54), No. 6, June 2016, pp. 3208-3220.
IEEE DOI 1606
hyperspectral imaging BibRef

Gu, Y., Liu, T., Jia, X., Benediktsson, J.A., Chanussot, J.,
Nonlinear Multiple Kernel Learning With Multiple-Structure-Element Extended Morphological Profiles for Hyperspectral Image Classification,
GeoRS(54), No. 6, June 2016, pp. 3235-3247.
IEEE DOI 1606
geophysical image processing BibRef

Cui, M.[Minshan], Prasad, S.[Saurabh],
Class-Dependent Sparse Representation Classifier for Robust Hyperspectral Image Classification,
GeoRS(53), No. 5, May 2015, pp. 2683-2695.
IEEE DOI 1502
correlation theory BibRef

Cui, M.[Minshan], Prasad, S.[Saurabh],
Sparse representation-based classification: Orthogonal least squares or orthogonal matching pursuit?,
PRL(84), No. 1, 2016, pp. 120-126.
Elsevier DOI 1612
Orthogonal least square BibRef

Tuia, D.[Devis], Flamary, R.[Rémi], Courty, N.[Nicolas],
Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions,
PandRS(105), No. 1, 2015, pp. 272-285.
Elsevier DOI 1506
Hyperspectral imaging BibRef

Courty, N.[Nicolas], Flamary, R.[Rémi], Tuia, D.[Devis], Rakotomamonjy, A.,
Optimal Transport for Domain Adaptation,
PAMI(39), No. 9, September 2017, pp. 1853-1865.
IEEE DOI 1708
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Earlier: A3, A2, A4, A1:
Multitemporal classification without new labels: A solution with optimal transport,
MultiTemp15(1-4)
IEEE DOI 1511
Data analysis, Feature extraction, Kernel, Probability density function, Probability distribution, Training, Transportation, Unsupervised domain adaptation, classification, optimal transport, transfer learning, visual adaptation. geophysical image processing BibRef

Rebetez, J.[Julien], Tuia, D.[Devis], Courty, N.[Nicolas],
Network-Based Correlated Correspondence for Unsupervised Domain Adaptation of Hyperspectral Satellite Images,
ICPR14(3921-3926)
IEEE DOI 1412
Approximation algorithms. Adapt to changes in data distribution. BibRef

Boesche, N.K.[Nina Kristine], Rogass, C.[Christian], Lubitz, C.[Christin], Brell, M.[Maximilian], Herrmann, S.[Sabrina], Mielke, C.[Christian], Tonn, S.[Sabine], Appelt, O.[Oona], Altenberger, U.[Uwe], Kaufmann, H.[Hermann],
Hyperspectral REE (Rare Earth Element) Mapping of Outcrops: Applications for Neodymium Detection,
RS(7), No. 5, 2015, pp. 5160-5186.
DOI Link 1506
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Jia, M.[Meng], Gong, M.[Maoguo], Jiao, L.C.[Li-Cheng],
Hyperspectral image classification using discontinuity adaptive class-relative nonlocal means and energy fusion strategy,
PandRS(106), No. 1, 2015, pp. 16-27.
Elsevier DOI 1507
Change detection BibRef

Jung, A.[András], Vohland, M.[Michael], Thiele-Bruhn, S.[Sören],
Use of a Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data,
RS(7), No. 9, 2015, pp. 11434.
DOI Link 1511
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Notesco, G.[Gila], Ogen, Y.[Yaron], Ben-Dor, E.[Eyal],
Mineral Classification of Makhtesh Ramon in Israel Using Hyperspectral Longwave Infrared (LWIR) Remote-Sensing Data,
RS(7), No. 9, 2015, pp. 12282.
DOI Link 1511
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Notesco, G.[Gila], Ogen, Y.[Yaron], Ben-Dor, E.[Eyal],
Integration of Hyperspectral Shortwave and Longwave Infrared Remote-Sensing Data for Mineral Mapping of Makhtesh Ramon in Israel,
RS(8), No. 4, 2016, pp. 318.
DOI Link 1604
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Abdel-Rahman, E.M.[Elfatih M.], Makori, D.M.[David M.], Landmann, T.[Tobias], Piiroinen, R.[Rami], Gasim, S.[Seif], Pellikka, P.[Petri], Raina, S.K.[Suresh K.],
The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping,
RS(7), No. 10, 2015, pp. 13298.
DOI Link 1511
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Bannon, D.[David],
Hyperspectral, multispectral technologies find commercial applications,
SPIE(Newsroom), July 6, 2015
DOI Link 1511
First developed for military use, hyperspectral imaging now brings in-line inspection of foods and consumer products to new levels of accuracy. Its applications extend beyond the production line as well, says Headwall's CEO. BibRef

Bauer, S.[Sebastian], León, F.P.[Fernando Puente],
Hyperspectral fluorescence imaging for mineral classification,
30 July 2015, SPIE Newsroom. DOI: SPIE(Newsroom), July 30, 2015
DOI Link 1511
A novel approach can be used for industrial sorting and presents several advantages over conventional hyperspectral imaging techniques. BibRef

Peng, B.[Bing], Li, W.[Wei], Xie, X.M.[Xiao-Ming], Du, Q.[Qian], Liu, K.[Kui],
Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery,
RS(7), No. 11, 2015, pp. 14806.
DOI Link 1512
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Sidike, P., Asari, V.K., Alam, M.S.,
Multiclass Object Detection With Single Query in Hyperspectral Imagery Using Class-Associative Spectral Fringe-Adjusted Joint Transform Correlation,
GeoRS(54), No. 2, February 2016, pp. 1196-1208.
IEEE DOI 1601
Correlation BibRef

Toksöz, M.A., Ulusoy, I.,
Hyperspectral Image Classification via Basic Thresholding Classifier,
GeoRS(54), No. 7, July 2016, pp. 4039-4051.
IEEE DOI 1606
Computational efficiency BibRef

Toksöz, M.A., Ulusoy, I.,
Hyperspectral Image Classification via Kernel Basic Thresholding Classifier,
GeoRS(55), No. 2, February 2017, pp. 715-728.
IEEE DOI 1702
hyperspectral imaging BibRef

Zhao, C., Gao, X., Wang, Y., Li, J.,
Efficient Multiple-Feature Learning-Based Hyperspectral Image Classification With Limited Training Samples,
GeoRS(54), No. 7, July 2016, pp. 4052-4062.
IEEE DOI 1606
Bayes methods BibRef

Zehtabian, A., Ghassemian, H.,
Automatic Object-Based Hyperspectral Image Classification Using Complex Diffusions and a New Distance Metric,
GeoRS(54), No. 7, July 2016, pp. 4106-4114.
IEEE DOI 1606
Diffusion processes BibRef

Wei, Y.[Yantao], Zhou, Y.[Yicong], Li, H.[Hong],
Spectral-Spatial Response for Hyperspectral Image Classification,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
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Zhou, Y.[Yicong], Wei, Y.[Yantao],
Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification,
Cyber(46), No. 7, July 2016, pp. 1667-1678.
IEEE DOI 1606
Accuracy BibRef

Li, H.[Hong], Song, Y.[Yalong], Chen, C.L.P.[C.L. Philip],
Hyperspectral Image Classification Based on Multiscale Spatial Information Fusion,
GeoRS(55), No. 9, September 2017, pp. 5302-5312.
IEEE DOI 1709
hyperspectral imaging, image classification, image fusion, HSI classification, L1-DE, MSIF, hyperspectral image classification, local 1D embedding, multiscale spatial information fusion, multiscale strategy, spatial information, spectral information, spectral-spatial classification method, Dictionaries, Feature extraction, Hyperspectral imaging, Kernel, Support vector machines, Hyperspectral image (HSI) classification, local 1-D embedding (L1-DE), multiscale, spatial, information BibRef

Zhong, Z., Fan, B., Ding, K., Li, H., Xiang, S., Pan, C.,
Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study,
GeoRS(54), No. 8, August 2016, pp. 4461-4478.
IEEE DOI 1608
feature extraction BibRef

Wu, H., Prasad, S.,
Dirichlet Process Based Active Learning and Discovery of Unknown Classes for Hyperspectral Image Classification,
GeoRS(54), No. 8, August 2016, pp. 4882-4895.
IEEE DOI 1608
geophysical image processing BibRef

Lu, X.C.[Xiao-Chen], Zhang, J.P.[Jun-Ping], Li, T.[Tong], Zhang, Y.[Ye],
A Novel Synergetic Classification Approach for Hyperspectral and Panchromatic Images Based on Self-Learning,
GeoRS(54), No. 8, August 2016, pp. 4917-4928.
IEEE DOI 1608
hyperspectral imaging BibRef

Lu, X.C.[Xiao-Chen], Zhang, J.P.[Jun-Ping], Li, T.[Tong], Zhang, Y.[Ye],
Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images,
RS(8), No. 10, 2016, pp. 804.
DOI Link 1609
BibRef

Lu, X.C.[Xiao-Chen], Zhang, J.P.[Jun-Ping], Li, T.[Tong], Zhang, Y.[Ye],
Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
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Chang, C.I., Li, Y.,
Recursive Band Processing of Automatic Target Generation Process for Finding Unsupervised Targets in Hyperspectral Imagery,
GeoRS(54), No. 9, September 2016, pp. 5081-5094.
IEEE DOI 1609
geophysical image processing BibRef

Peralta, B.[Billy], Caro, A.[Alberto], Soto, A.[Alvaro],
A proposal for supervised clustering with Dirichlet Process using labels,
PRL(80), No. 1, 2016, pp. 52-57.
Elsevier DOI 1609
Dirichlet Process BibRef

Kianisarkaleh, A.[Azadeh], Ghassemian, H.[Hassan],
Nonparametric feature extraction for classification of hyperspectral images with limited training samples,
PandRS(119), No. 1, 2016, pp. 64-78.
Elsevier DOI 1610
Nonparametric feature extraction BibRef

Imani, M., Ghassemian, H.,
Boundary Based Supervised Classification of Hyperspectral Images with Limited Training Samples,
SMPR13(203-207).
HTML Version. 1311
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Ma, X.R.[Xiao-Rui], Wang, H.Y.[Hong-Yu], Wang, J.[Jie],
Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning,
PandRS(120), No. 1, 2016, pp. 99-107.
Elsevier DOI 1610
Hyperspectral image BibRef

Axelsson, M.[Maria], Friman, O.[Ola], Haavardsholm, T.V.[Trym Vegard], Renhorn, I.[Ingmar],
Target detection in hyperspectral imagery using forward modeling and in-scene information,
PandRS(119), No. 1, 2016, pp. 124-134.
Elsevier DOI 1610
Hyperspectral imaging BibRef

Wang, Y.[Yi], Song, H.W.[Hai-Wei], Zhang, Y.[Yan],
Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model,
RS(8), No. 9, 2016, pp. 748.
DOI Link 1610
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Appice, A.[Annalisa], Guccione, P.[Pietro], Malerba, D.[Donato],
A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data,
PR(63), No. 1, 2017, pp. 229-245.
Elsevier DOI 1612
Hyperspectral imagery classification BibRef

Arablouei, R., de Hoog, F.,
Hyperspectral Image Recovery via Hybrid Regularization,
IP(25), No. 12, December 2016, pp. 5649-5663.
IEEE DOI 1612
convergence of numerical methods BibRef

Xia, J.S.[Jun-Shi], Yokoya, N.[Naoto], Iwasaki, A.[Akira],
Hyperspectral Image Classification With Canonical Correlation Forests,
GeoRS(55), No. 1, January 2017, pp. 421-431.
IEEE DOI 1701
Markov processes BibRef

Li, L.[Lu], Wang, C.[Chengyi], Chen, J.[Jingbo], Ma, J.[Jianglin],
Refinement of Hyperspectral Image Classification with Segment-Tree Filtering,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Liang, J.[Jie], Zhou, J.[Jun], Qian, Y.T.[Yun-Tao], Wen, L.[Lian], Bai, X.[Xiao], Gao, Y.S.[Yong-Sheng],
On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification,
GeoRS(55), No. 2, February 2017, pp. 862-880.
IEEE DOI 1702
hyperspectral imaging BibRef

Li, F.[Feng], Xin, L.[Lei], Guo, Y.[Yi], Gao, J.B.[Jun-Bin], Jia, X.P.[Xiu-Ping],
A Framework of Mixed Sparse Representations for Remote Sensing Images,
GeoRS(55), No. 2, February 2017, pp. 1210-1221.
IEEE DOI 1702
hyperspectral imaging. combination of subimage of smooth, edges, and point-like components. BibRef

Ni, D., Ma, H.,
Fast Classification of Hyperspectral Images Using Globally Regularized Archetypal Representation With Approximate Solution,
GeoRS(55), No. 4, April 2017, pp. 2414-2430.
IEEE DOI 1704
approximation theory BibRef

Fu, Y.Y.[Yuan-Yuan], Zhao, C.J.[Chun-Jiang], Wang, J.[Jihua], Jia, X.P.[Xiu-Ping], Yang, G.J.[Gui-Jun], Song, X.[Xiaoyu], Feng, H.K.[Hai-Kuan],
An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Liu, Y., Gao, G., Gu, Y.,
Tensor Matched Subspace Detector for Hyperspectral Target Detection,
GeoRS(55), No. 4, April 2017, pp. 1967-1974.
IEEE DOI 1704
geophysical image processing BibRef

Zhu, W., Chayes, V., Tiard, A., Sanchez, S., Dahlberg, D., Bertozzi, A.L., Osher, S., Zosso, D., Kuang, D.,
Unsupervised Classification in Hyperspectral Imagery With Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm,
GeoRS(55), No. 5, May 2017, pp. 2786-2798.
IEEE DOI 1705
geophysical image processing, hyperspectral imaging, image classification, pattern clustering, Merriman-Bence-Osher scheme, graph based nonlocal total variation method, hyperspectral imagery, hyperspectral images, labeling function, primal dual hybrid gradient algorithm, random initialization, stable simplex clustering routine, unsupervised classification, unsupervised clustering method, variational problem, Clustering algorithms, Clustering methods, Hyperspectral imaging, Image processing, Labeling, Sparse matrices, TV, Hyperspectral images (HSI), nonlocal total variation (NLTV), primal-dual hybrid gradient (PDHG) algorithm, stable simplex clustering, unsupervised, classification BibRef

Zhong, P.[Ping], Gong, Z.Q.[Zhi-Qiang], Li, S.T.[Shu-Tao], Schönlieb, C.B.[Carola-Bibiane],
Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification,
GeoRS(55), No. 6, June 2017, pp. 3516-3530.
IEEE DOI 1706
BibRef
Earlier: A1, A2, A4, Only:
A Diversified Deep Belief Network For Hyperspectral Image Classification,
ISPRS16(B7: 443-449).
DOI Link 1610
Feature extraction, Hidden Markov models, Hyperspectral imaging, Neurons, Training, Deep belief network (DBN), diversity, hyperspectral image, image, classification BibRef

Wang, Z., Du, B., Zhang, L., Zhang, L., Jia, X.,
A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification,
GeoRS(55), No. 6, June 2017, pp. 3071-3083.
IEEE DOI 1706
Hyperspectral imaging, Labeling, Semisupervised learning, Training, Uncertainty, Active learning, hyperspectral classification, semisupervised, learning BibRef

Sun, Y.[Yubao], Wang, S.J.[Su-Juan], Liu, Q.S.[Qing-Shan], Hang, R.L.[Ren-Long], Liu, G.C.[Guang-Can],
Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Wan, X.Q.[Xiao-Qing], Zhao, C.H.[Chun-Hui],
Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images,
JOSA-A(34), No. 6, June 2017, pp. 1011-1020.
DOI Link 1706
Image processing, Image analysis, Remote, sensing, and, sensors BibRef

Yin, J., Qv, H., Luo, X., Jia, X.,
Segment-Oriented Depiction and Analysis for Hyperspectral Image Data,
GeoRS(55), No. 7, July 2017, pp. 3982-3996.
IEEE DOI 1706
Dictionaries, Encoding, Hyperspectral imaging, Image segmentation, Matching pursuit algorithms, Training, Dictionary learning, hyperspectral image (HSI) classification, segment, sparse, representation BibRef

Pan, B., Shi, Z., Xu, X.,
Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images,
GeoRS(55), No. 7, July 2017, pp. 4177-4189.
IEEE DOI 1706
Data mining, Feature extraction, Hyperspectral imaging, Image edge detection, Support vector machines, Training, Ensemble learning, hierarchical guidance filtering (HGF), hyperspectral, image, (HSI), classification BibRef

Gao, L.[Lianru], Zhao, B.[Bin], Jia, X.P.[Xiu-Ping], Liao, W.Z.[Wen-Zhi], Zhang, B.[Bing],
Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Meng, Z.[Zhaoyi], Merkurjev, E.[Ekaterina], Koniges, A.[Alice], Bertozzi, A.L.[Andrea L.],
Hyperspectral Image Classification Using Graph Clustering Methods,
IPOL(7), 2017, pp. 218-245.
DOI Link 1708
Code, Hyperspectral Classification. Initial description: See also Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme. See also Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video. Parallel Implementation: See also OpenMP parallelization and optimization of graph-based machine learning algorithms. BibRef

Wang, Z.[Ziyu], Zhu, R.[Rui], Fukui, K.[Kazuhiro], Xue, J.H.[Jing-Hao],
Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection,
IP(26), No. 11, November 2017, pp. 5447-5461.
IEEE DOI 1709
Bayes methods, Hyperspectral imaging, Object detection, cone representation. BibRef

Singhal, V., Aggarwal, H.K., Tariyal, S., Majumdar, A.,
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification,
GeoRS(55), No. 9, September 2017, pp. 5274-5283.
IEEE DOI 1709
deep belief network, hyperspectral image classification, linear classifier, BibRef

Jiao, L., Liang, M., Chen, H., Yang, S., Liu, H., Cao, X.,
Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification,
GeoRS(55), No. 10, October 2017, pp. 5585-5599.
IEEE DOI 1710
convolution, feature extraction, hyperspectral imaging, image fusion, neural nets, statistics, HSIC, deep fully convolutional network, BibRef

Stevens, J.R., Resmini, R.G., Messinger, D.W.,
Spectral-Density-Based Graph Construction Techniques for Hyperspectral Image Analysis,
GeoRS(55), No. 10, October 2017, pp. 5966-5983.
IEEE DOI 1710
data mining, edge detection, graph theory, hyperspectral imaging, remote sensing, HSI, data mining, density-based edge allocation, density-weighted graph construction, derived manifold coordinates, graph theory, BibRef

Prabhakar, T.V.N.[T.V. Nidhin], Geetha, P.,
Two-dimensional empirical wavelet transform based supervised hyperspectral image classification,
PandRS(133), No. Supplement C, 2017, pp. 37-45.
Elsevier DOI 1711
Image empirical mode decomposition, Empirical wavelet transform, Hyperspectral image classification, Feature extraction, Subspace pursuit, Orthogonal matching pursuit, Support vector machines BibRef

Tong, F.[Fei], Tong, H.[Hengjian], Jiang, J.[Junjun], Zhang, Y.[Yun],
Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef


Zhong, P., Gong, Z.Q.[Zhi-Qiang], Schönlieb, C.B.,
A DBN-CRF for spectral-spatial classification of hyperspectral data,
ICPR16(1219-1224)
IEEE DOI 1705
Context modeling, Feature extraction, Hidden Markov models, Hyperspectral imaging, Image classification, Linear programming, Training, Conditional random field, Contextual information, Deep belief network, Deep learning, Hyperspectral, image BibRef

Bo, C.J.[Chun-Juan], Wang, D.[Dong], Lu, H.C.[Hu-Chuan],
Hyperspectral Image Classification via a Joint Weighted K-Nearest Neighbour Approach,
HISP16(I: 349-360).
Springer DOI 1704
BibRef

Shen, Y.[Yu], Xiao, L.[Liang], Molaei, M.[Mohsen],
Joint Multiview Fused ELM Learning with Propagation Filter for Hyperspectral Image Classification,
HISP16(I: 374-388).
Springer DOI 1704
BibRef

Han, D., Du, Q., Younan, N.H.,
Semisupervised classification of hyperspectral remote sensing images with spatial majority voting,
PRRS16(1-4)
IEEE DOI 1704
hyperspectral imaging BibRef

Petersson, H., Gustafsson, D., Bergstrom, D.,
Hyperspectral image analysis using deep learning: A review,
IPTA16(1-6)
IEEE DOI 1703
feature extraction BibRef

Becek, K., Borkowski, A., Mekik, Ç.,
A Study Of The Impact Of Insolation On Remote Sensing-based Landcover And Landuse Data Extraction,
ISPRS16(B7: 65-69).
DOI Link 1610
normalized total insolation index (NTII) from lidar. Estimate pixel reflectance dependency. BibRef

Movia, A., Beinat, A., Sandri, T.,
Land Use Classification from VHR Aerial Images Using Invariant Colour Components And Texture,
ISPRS16(B7: 311-317).
DOI Link 1610
BibRef

Gewali, U.B., Monteiro, S.T.,
A novel covariance function for predicting vegetation biochemistry from hyperspectral imagery with Gaussian processes,
ICIP16(2216-2220)
IEEE DOI 1610
Gaussian processes BibRef

Xu, Y., Wu, Z., Wei, Z., Dalla Mura, M., Chanussot, J., Bertozzi, A.,
GAS plume detection in hyperspectral video sequence using low rank representation,
ICIP16(2221-2225)
IEEE DOI 1610
Decision support systems BibRef

Franchi, G., Angulo, J.,
A deep spatial/spectral descriptor of hyperspectral texture using scattering transform,
ICIP16(3568-3572)
IEEE DOI 1610
Hyperspectral imaging BibRef

Franchi, G., Angulo, J., Sejdinovic, D.,
Hyperspectral image classification with support vector machines on kernel distribution embeddings,
ICIP16(1898-1902)
IEEE DOI 1610
Hilbert space BibRef

Shurygin, B., Shestakova, M., Nikolenko, A., Badasen, E., Strakhov, P.,
Accounting For Variance In Hyperspectral Data Coming From Limitations Of The Imaging System,
ISPRS16(B7: 365-369).
DOI Link 1610
BibRef

Savorskiy, V., Loupian, E., Balashov, I., Kashnitskii, A., Konstantinova, A., Tolpin, V., Uvarov, I., Kuznetsov, O., Maklakov, S., Panova, O., Savchenko, E.,
Vega-constellation Tools To Analize Hyperspectral Images,
ISPRS16(B4: 235-242).
DOI Link 1610
BibRef

Kurz, T.H., Buckley, S.J.,
A Review Of Hyperspectral Imaging In Close Range Applications,
ISPRS16(B5: 865-870).
DOI Link 1610
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Honkavaara, E., Hakala, T., Nevalainen, O., Viljanen, N., Rosnell, T., Khoramshahi, E., Näsi, R., Oliveira, R., Tommaselli, A.,
Geometric And Reflectance Signature Characterization Of Complex Canopies Using Hyperspectral Stereoscopic Images From UAV And Terrestrial Platforms,
ISPRS16(B7: 77-82).
DOI Link 1610
BibRef

Walczykowski, P., Jenerowicz, A., Orych, A., Siok, K.,
Determining Spectral Reflectance Coefficients From Hyperspectral Images Obtained From Low Altitudes,
ISPRS16(B7: 107-110).
DOI Link 1610
BibRef

Kumar, B., Dikshit, O.,
Parallel Implementation Of Morphological Profile Based Spectral-spatial Classification Scheme For Hyperspectral Imagery,
ISPRS16(B7: 263-267).
DOI Link 1610
BibRef

Liu, C.Y., Ren, H.,
Multiple Reflection Effects In Nonlinear Mixture Model For Hyperspectral Image Analysis,
ISPRS16(B7: 295-297).
DOI Link 1610
BibRef

Hoang, N.T.[Nguyen Tien], Koike, K.[Katsuaki],
Hyperspectral Transformation From Eo-1 Ali Imagery Using Pseudo-hyperspectral Image Synthesis Algorithm,
ISPRS16(B7: 661-665).
DOI Link 1610
BibRef

Zhang, Y., Huynh, C.P., Habili, N., Ngan, K.N.,
Material segmentation in hyperspectral images with minimal region perimeters,
ICIP16(834-838)
IEEE DOI 1610
Hyperspectral imaging BibRef

Walczykowski, P., Siok, K., Jenerowicz, A.,
Methodology For Determining Optimal Exposure Parameters Of A Hyperspectral Scanning Sensor,
ISPRS16(B1: 1065-1069).
DOI Link 1610
BibRef

Ziemann, A.K.[Amanda K.], Theiler, J.[James], Messinger, D.W.[David W.],
Hyperspectral target detection using manifold learning and multiple target spectra,
AIPR15(1-7)
IEEE DOI 1605
graph theory BibRef

Le, J.H.[Justin H.], Yazdanpanah, A.P.[Ali Pour], Regentova, E.E.[Emma E.], Muthukumar, V.[Venkatesan],
A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data,
ISVC15(I: 682-692).
Springer DOI 1601
BibRef

Santos, A.B.[Andrey Bicalho], de Albuquerque Araujo, A.[Arnaldo], Schwartz, W.R.[William Robson], Menotti, D.[David],
Hyperspectral image interpretation based on partial least squares,
ICIP15(1885-1889)
IEEE DOI 1512
Extended morphological profile BibRef

Ahmad, O.[Ola], Collet, C.[Christophe], Salzenstein, F.[Fabien],
Spatio-spectral Gaussian random field modeling approach for target detection on hyperspectral data obtained in very low SNR,
ICIP15(2090-2094)
IEEE DOI 1512
Expected Euler-characteristic BibRef

Menon, V.[Vineetha], Prasad, S.[Saurabh], Fowler, J.E.[James E.],
Hyperspectral classification using a composite kernel driven by nearest-neighbor spatial features,
ICIP15(2100-2104)
IEEE DOI 1512
composite kernel; hyperspectral classification; nearest neighbor BibRef

Liu, Y.Z.[Ya-Zhou], Cao, G.[Guo], Sun, Q.S.[Quan-Sen], Siegel, M.[Mel],
Hyperspectral classification via learnt features,
ICIP15(2591-2595)
IEEE DOI 1512
Deep learning BibRef

Ni, D.[Ding], Ma, H.B.[Hong-Bing],
A sample set perspective on the classification of hyperspectral image with weighted affine constraint,
ICIP15(581-585)
IEEE DOI 1512
Hyperspectral image. Add neighbors to the sample set. BibRef

Jia, S.[Sen], Zhang, X.J.[Xiu-Jun], Deng, L.[Lin], Shu, Z.Q.[Zhen-Qiu],
An L_1/2 regularized low-rank representation for hyperspectral imagery classification,
ICIP15(1777-1780)
IEEE DOI 1512
Hyperspectral imagery classification; low-rank representation BibRef

Feng, S.W.[Si-Wei], Duarte, M.F.[Marco F.], Parente, M.[Mario],
Universality of wavelet-based non-homogeneous hidden Markov chain model features for hyperspectral signatures,
EarthObserv15(19-27)
IEEE DOI 1510
Hidden Markov models BibRef

Pervez, W., Khan, S.A., Valiuddin,
Hyperspectral Hyperion Imagery Analysis and Its Application Using Spectral Analysis,
PIA15(169-175).
DOI Link 1504
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Li, T.[Tong], Zhang, J.P.[Jun-Ping], Zhang, Y.[Ye],
Classification of hyperspectral image based on deep belief networks,
ICIP14(5132-5136)
IEEE DOI 1502
Accuracy BibRef

Holloway, J.[Jason], Priya, T.[Tanu], Veeraraghavan, A.[Ashok], Prasad, S.[Saurabh],
Image classification in natural scenes: Are a few selective spectral channels sufficient?,
ICIP14(655-659)
IEEE DOI 1502
Accuracy. Does hyperspectral really add anything? Maybe not. BibRef

Fowler, J.E.[James E.],
Compressive pushbroom and whiskbroom sensing for hyperspectral remote-sensing imaging,
ICIP14(684-688)
IEEE DOI 1502
Hyperspectral imaging BibRef

Li, H.C.[Hai-Chang], Duan, J.Y.[Jiang-Yong], Xiang, S.M.[Shi-Ming], Wang, L.F.[Ling-Feng], Pan, C.H.[Chun-Hong],
Local Label Probability Propagation for Hyperspectral Image Classification,
ICPR14(4251-4256)
IEEE DOI 1412
Accuracy BibRef

Tong, L.[Lei], Zhou, J.[Jun], Xu, C.Y.[Cheng-Yuan], Qian, Y.T.[Yun-Tao], Gao, Y.S.[Yong-Sheng],
Soil Biochar Quantification via Hyperspectral Unmixing,
DICTA13(1-8)
IEEE DOI 1402
charcoal BibRef

Akbari, D., Safari, A.R.,
Rule-Based Classification of a Hyperspectral Image Using MSSC Hierarchical Segmentation,
SMPR13(13-18).
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Niazmardi, S., Safari, A., Homayouni, S.,
Maximum Margin Clustering of Hyperspectral Data,
SMPR13(305-308).
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Sayedain, S.A., Valadan Zouj, M.J., Maghsoudi, Y.,
Exploration of Oil Seepages Using Target Detection Algorithms in Hyperspectral Images,
SMPR13(361-363).
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Courty, N.[Nicolas], Aptoula, E.[Erchan], Lefevre, S.[Sebastien],
A classwise supervised ordering approach for morphology based hyperspectral image classification,
ICPR12(1997-2000).
WWW Link. 1302
BibRef

Pieper, M., Manolakis, D., Truslow, E., Cooley, T., Lipson, S.,
Performance evaluation of cluster-based hyperspectral target detection algorithms,
ICIP12(2669-2672).
IEEE DOI 1302
BibRef

Peng, H.H.[Hong-Hong], Rao, R.[Raghuveer], Dianat, S.A.[Sohail A.],
Nonnegative matrix factorization with deterministic annealing for unsupervised unmixing of hyperspectral imagery,
ICIP12(2145-2148).
IEEE DOI 1302
BibRef

Huang, R.[Rui], He, W.Y.[Wen-Yong],
Using tri-training to exploit spectral and spatial information for hyperspectral data classification,
CVRS12(30-33).
IEEE DOI 1302
BibRef

Liu, L.[Liu], Shi, Z.W.[Zhen-Wei], Yang, S.[Shuo], Zhang, H.[Haohan],
Robust high-order matched filter for hyperspectral target detection with quasi-Newton method,
CVRS12(63-66).
IEEE DOI 1302
BibRef

Liao, W.Z.[Wen-Zhi], Bellens, R.[Rik], Pižurica, A.[Aleksandra], Philips, W.[Wilfried], Pi, Y.G.[You-Guo],
Classification of Hyperspectral Data over Urban Areas Based on Extended Morphological Profile with Partial Reconstruction,
ACIVS12(278-289).
Springer DOI 1209
BibRef

Lee, J.D., Dewitt, B.A., Lee, S.S., Bhang, K.J., Sim, J.B.,
Analysis of Concrete Reflectance Characteristics Using Spectrometer and VNIR Hyperspectral Camera,
ISPRS12(XXXIX-B7:127-130).
DOI Link 1209
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Chisense, C.,
Classification of Roof Materials Using Hyperspectral Data,
ISPRS12(XXXIX-B7:103-107).
DOI Link 1209
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Cong, L.[Lin], Nutter, B.[Brian], Liang, D.[Daan],
Estimation of oil thickness and aging from hyperspectral signature,
Southwest12(213-216).
IEEE DOI 1205
BibRef

Gormus, E.T.[Esra Tunc], Canagarajah, N.[Nishan], Achim, A.[Alin],
Dimensionality reduction of hyperspectral images with wavelet based Empirical Mode Decomposition,
ICIP11(1709-1712).
IEEE DOI 1201
BibRef

Bachega, L.R.[Leonardo R.], Bouman, C.A.[Charles A.],
Classification of high-dimensional data using the Sparse Matrix Transform,
ICIP10(265-268).
IEEE DOI 1009
BibRef

Krishnamurthy, K.[Kalyani], Raginsky, M.[Maxim], Willett, R.M.[Rebecca M.],
Hyperspectral target detection from incoherent projections: Nonequiprobable targets and inhomogeneous SNR,
ICIP10(1357-1360).
IEEE DOI 1009
BibRef

Li, X.K.[Xiao-Kun],
Detecting subpixel targets in Hyperspectral images via knowledgeaided adaptive filtering,
ICIP10(1365-1368).
IEEE DOI 1009
BibRef

Martin-Herrero, J.[Julio], Ferreiro-Arman, M.[Marcos],
Tensor-Driven Hyperspectral Denoising: A Strong Link for Classification Chains?,
ICPR10(2820-2823).
IEEE DOI 1008
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Hasani, H.[Hadiseh],
Sensitivity analysis of support vector machine in classification of hyperspectral imagery,
CGC10(187).
PDF File. 1006
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Nackaerts, K., Delauré, B., Everaerts, J., Michiels, B., Holmund, C., Mäkynen, J., Saari, H.,
Evaluation Of A Lightweigth Uas-prototype For Hyperspectral Imaging.,
CloseRange10(xx-yy).
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Nielsen, A.A.[Allan Aasbjerg],
Kernel methods in orthogonalization of multi-and hypervariate data,
ICIP09(3729-3732).
IEEE DOI 0911
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Li, J.M.[Ji-Ming], Hu, Z.F.[Zhen-Fang], Qian, Y.T.[Yun-Tao],
Hyperspectral data classification using Margin Infused Relaxed Algorithm,
ICIP09(1689-1692).
IEEE DOI 0911
BibRef

Li, J.M.[Ji-Ming], Qian, Y.T.[Yun-Tao], Jia, S.[Sen],
Regularized logistic regression method for change detection in multispectral data via Pathwise Coordinate optimization,
ICIP10(2309-2312).
IEEE DOI 1009
BibRef

Li, J.M.[Ji-Ming], Qian, Y.T.[Yun-Tao],
Regularized Multinomial Regression Method for Hyperspectral Data Classification via Pathwise Coordinate Optimization,
DICTA09(540-545).
IEEE DOI 0912
BibRef

Mayer, R., Edwards, J., Antoniades, J.,
Segmentation approach and comparison to hyperspectral object detection algorithms,
AIPR05(36-41).
IEEE DOI 0510
BibRef

Gupta, N.[Neelam],
Development of spectropolarimetric imagers for imaging of desert soils,
AIPR14(1-7)
IEEE DOI 1504
BibRef
And:
Development of staring hyperspectral imagers,
AIPR11(1-8).
IEEE DOI 1204
acousto-optical filters BibRef

Gupta, N.,
Fused spectropolarimetric visible near-IR imaging,
AIPR03(21-26).
IEEE DOI 0310
BibRef

Hinnrichs, M., Gupta, N., Goldberg, A.,
Dual band (MWIR/LWIR) hyperspectral imager,
AIPR03(73-78).
IEEE DOI 0310
BibRef

Gupta, N., Smith, D.,
A field-portable simultaneous dual-band infrared hyperspectral imager,
AIPR05(87-92).
IEEE DOI 0510
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Ramanath, R., Snyder, W.E., Qi, H.R.[Hai-Rong],
Eigenviews for object recognition in multispectral imaging systems,
AIPR03(33-38).
IEEE DOI 0310
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Schaum, A.P., Stocker, A.,
Advanced algorithms for autonomous hyperspectral change detection,
AIPR04(33-38).
IEEE DOI 0410
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Schaum, A.P.,
Algorithms with attitude,
AIPR10(1-6).
IEEE DOI 1010
BibRef

Schaum, A.P.,
Advanced hyperspectral detection based on elliptically contoured distribution models and operator feedback,
AIPR09(1-5).
IEEE DOI 0910
BibRef

Schaum, A.P.,
Adapting to Change: The CFAR Problem in Advanced Hyperspectral Detection,
AIPR07(15-21).
IEEE DOI 0710
BibRef

Schaum, A.P.,
Autonomous Hyperspectral Target Detection with Quasi-Stationarity Violation at Background Boundaries,
AIPR06(16-16).
IEEE DOI 0610
BibRef
Earlier:
Hyperspectral detection algorithms: operational, next generation, on the horizon,
AIPR05(72-80).
IEEE DOI 0510
BibRef
Earlier:
Matched affine joint subspace detection in remote hyperspectral reconnaissance,
AIPR02(13-18).
IEEE DOI 0210
BibRef

Schaum, A.P.,
Bayesian solutions to non-Bayesian detection problems: Unification through fusion,
AIPR14(1-4)
IEEE DOI 1504
Bayes methods BibRef

Schaum, A.P.,
Data association for fusion in spatial and spectral imaging,
AIPR03(87-92).
IEEE DOI 0310
BibRef

Shah, C.A., Arora, M.K., Robila, S.A., Varshney, P.K.,
ICA mixture model based unsupervised classification of hyperspectral imagery,
AIPR02(29-35).
IEEE DOI 0210
BibRef

Schott, J.R., Lee, K., Raqueno, R., Hoffmann, G.,
Use of physics based models in hyperspectral image exploitation,
AIPR02(36-42).
IEEE DOI 0210
BibRef

Muhammed, H.H.,
Unsupervised hyperspectral image segmentation using a new class of neuro-fuzzy systems based on weighted incremental neural networks,
AIPR02(171-177).
IEEE DOI 0210
BibRef
And:
Using hyperspectral reflectance data for discrimination between healthy and diseased plants, and determination of damage-level in diseased plants,
AIPR02(49-54).
IEEE DOI 0210
BibRef

Dombrowski, M., Bajaj, J., Willson, P.,
Video-rate visible to LWIR hyperspectral imaging and image exploitation,
AIPR02(178-185).
IEEE DOI 0210
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Streeter, L., Burling-Claridge, G.R., Cree, M.J., Kunnemeyer, R.,
Comparison of Hadamard imaging and compressed sensing for low resolution hyperspectral imaging,
IVCNZ08(1-6).
IEEE DOI 0811
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Sato, M.[Maiko], Kudo, M.[Mineichi], Toyama, J.[Jun],
Behavior Analysis of Volume Prototypes in High Dimensionality,
SSPR08(874-884).
Springer DOI 0812
BibRef

Yang, H., Wang, Q., He, Z.,
Indexing Sub-Vector Distance for High-Dimensional Feature Matching,
BMVC08(xx-yy).
PDF File. 0809
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Gupta, M.R., Jacobson, N.P.,
Wavelet Principal Component Analysis and its Application to Hyperspectral Images,
ICIP06(1585-1588).
IEEE DOI 0610
BibRef

Bakir, T., Peter, A.M., Riley, R., Hackett, J.,
Non-Negative Maximum Likelihood ICA for Blind Source Separation of Images and Signals with Application to Hyperspectral Image Subpixel Demixing,
ICIP06(3237-3240).
IEEE DOI 0610
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Ferreiro-Armán, M., da Costa, J.P., Homayouni, S., Martín-Herrero, J.,
Hyperspectral Image Analysis for Precision Viticulture,
ICIAR06(II: 730-741).
Springer DOI 0610
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Borges, J.S.[Janete S.], Bioucas-Dias, J.M.[José M.], Marçal, A.R.S.[André R. S.],
Fast Sparse Multinomial Regression Applied to Hyperspectral Data,
ICIAR06(II: 700-709).
Springer DOI 0610
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Rothaus, K.[Kai], Jiang, X.Y.[Xiao-Yi], Lambers, M.[Martin],
Comparison of Methods for Hyperspherical Data Averaging and Parameter Estimation,
ICPR06(III: 395-399).
IEEE DOI 0609
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Nascimento, J.M.P.[José M.P.], Dias, J.M.B.[José M.B.],
Signal Subspace Identification in Hyperspectral Linear Mixtures,
IbPRIA05(II:207).
Springer DOI 0509
BibRef

Sarkar, S., Healey, G.,
Hyperspectral texture classification using generalized Markov fields,
CVPR04(I: 429-434).
IEEE DOI 0408
BibRef

Gomez Chova, L., Calpe, J., Soria, E., Camps Valls, G., Martin, J.D., Moreno, J.,
Cart-based feature selection of hyperspectral images for crop cover classification,
ICIP03(III: 589-592).
IEEE DOI 0312
BibRef

Gu, Y.F.[Yan-Feng], Zhang, Y.[Ye],
Unsupervised subspace linear spectral mixture analysis for hyperspectral images,
ICIP03(I: 801-804).
IEEE DOI 0312
BibRef

Gu, Y.F.[Yan-Feng], Zhang, Y.[Ye], Zhang, J.,
A Kernel Based Nonlinear Subspace Projection Method for Reduction of Hyperspectral, Image Dimensionality,
ICIP02(II: 357-360).
IEEE DOI 0210
BibRef

You, H., Chang, E.,
Spin Discriminant Analysis(SDA): Using A One-Dimensional Classifier for High Dimensional Classification Problems,
CVPR01(I:968-975).
IEEE DOI 0110
Using a simpler classifier to deal with harder (high-dimensional) problems. BibRef

Peng, J.[Jing], Heisterkamp, D.R.[Douglas R.], Dai, H.K.,
LDA/SVM Driven Nearest Neighbor Classification,
CVPR01(I:58-63).
IEEE DOI 0110
With high dimensions and limited samples. Neighbor morphing to eliminate the bias due to high dimensions. BibRef

Mostafa, M.G.H., Perkins, T.C., Farag, A.A.,
A Two-step Fuzzy-bayesian Classification for High Dimensional Data,
ICPR00(Vol III: 417-420).
IEEE DOI 0009
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Mostafa, M.G.H., Perkins, T.C., Farag, A.A.,
Supervised Fuzzy and Bayesian Classification of High Dimensional Data: a Comparative Study,
ICIP00(Vol I: 772-775).
IEEE DOI 0008
BibRef

Wu, S.G.[Shu-Guang], Desai, M.D.[Mita D.],
Adaptive tree-structured subspace classification of hyperspectral images,
ICIP98(I: 570-573).
IEEE DOI 9810
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Bajic, S.C.,
Accuracy of a supervised classification of the artificial objects in thermal hyperspectral images,
CIAP99(798-803).
IEEE DOI 9909
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data, Endmember Extraction .


Last update:Nov 11, 2017 at 13:31:57