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

Chapter Contents (Back)
Hyperspectral. Mixed Pixels: See also Hyperspectral Mixture Models, Mixed Pixels. See also Hyperspectral Data, Dimensionality Reduction, Band Selection. See also Hyperspectral Data, Endmember Extraction. See also Spectral-Spatial Classification, Hyperspectral Data.

Bryant, J.[Jack],
On the clustering of multidimensional pictorial data,
PR(11), No. 2, 1979, pp. 115-125.
Elsevier DOI 0309
BibRef

Eden, G., Gelsema, E.S.,
Investigation of multidimensional data using the interactive pattern analysis system ISPAHAN,
PR(11), No. 5-6, 1979, pp. 391-399.
Elsevier DOI 0309
BibRef

Gelsema, E.S., Eden, G.,
Mapping algorithms in ISPAHAN,
PR(12), No. 3, 1980, pp. 127-136.
Elsevier DOI 0309
White blood cells. BibRef

Gelsema, E.S., Timmers, T.,
An interactive implementation of nonparametric partitioning in ISPAHAN,
ICPR88(II: 1062-1064).
IEEE DOI 8811
BibRef

Curington, I.J.[Ian J.], Cannon, S.E.[Stephen E.],
Multiband image classification with a distributed architecture,
IVC(3), No. 2, May 1985, pp. 80-84.
Elsevier DOI 0401
BibRef

Green, A.,
A transformation for ordering multispectral data in terms of image quality with implications for noise removal,
GeoRS(26), No. 1, 1988, pp. 65-74. 1103
BibRef

Chen, C.C.T.[C.C. Thomas], and Landgrebe, D.A.[David A.],
A Spectral Feature Design System for the HIRIS/MODIS Era,
GeoRS(27), No. 6, November 1989, pp. 681-686.
IEEE Top Reference. BibRef 8911

Ismail, M.A.[Mohamed A.], Kamel, M.S.[Mohamed S.],
Multidimensional data clustering utilizing hybrid search strategies,
PR(22), No. 1, 1989, pp. 75-89.
Elsevier DOI 0309
BibRef

Yousri, N.A.[Noha A.], Kamel, M.S.[Mohamed S.], Ismail, M.A.[Mohamed A.],
A distance-relatedness dynamic model for clustering high dimensional data of arbitrary shapes and densities,
PR(42), No. 7, July 2009, pp. 1193-1209.
Elsevier DOI 0903
BibRef
And: Corrigendum: PR(45), No. 9, September 2012, pp. 3580-3582.
Elsevier DOI 1206
BibRef
Earlier:
A novel validity measure for clusters of arbitrary shapes and densities,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
Finding Arbitrary Shaped Clusters for Character Recognition,
ICIAR08(xx-yy).
Springer DOI 0806
Clustering; Dynamic model; Arbitrary shaped clusters; Arbitrary density clusters; High dimensional data; Distance-relatedness BibRef

Zhang, Q.W.[Qi-Wen], Wang, Q.R.[Qen Ring], Boyle, R.D.[Roger D.],
A clustering algorithm for data-sets with a large number of classes,
PR(24), No. 4, 1991, pp. 331-340.
Elsevier DOI 0401
BibRef

Aeberhard, S.[Stefan], Coomans, D.[Danny], de Vel, O.[Olivier],
Comparative analysis of statistical pattern recognition methods in high dimensional settings,
PR(27), No. 8, August 1994, pp. 1065-1077.
Elsevier DOI 0401
BibRef

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

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

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
BibRef

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
BibRef

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

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

Yuan, Y.[Yuan], Lin, J.Z.[Jian-Zhe], Wang, Q.[Qi],
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

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

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

Schweizer, S.M., Moura, J.M.F.,
Efficient detection in hyperspectral imagery,
IP(10), No. 4, April 2001, pp. 584-597.
IEEE DOI 0104
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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

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
BibRef

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.
Elsevier DOI 0307
BibRef

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.
Elsevier DOI 0309
BibRef

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.
Elsevier DOI
PDF File. 0311
BibRef

Bioucas-Dias, J.M.B.[José M.B.], Nascimento, J.M.P.[José 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.],
Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning,
GeoRS(49), No. 6, June 2011, pp. 2151-2164.
IEEE DOI 1106
BibRef
Earlier: IbPRIA07(I: 22-29).
Springer DOI 0706
BibRef

Bachmann, C.M., Ainsworth, T.L., Fusina, R.A.,
Exploiting Manifold Geometry in Hyperspectral Imagery,
GeoRS(43), No. 3, March 2005, pp. 441-454.
IEEE Abstract. 0501
BibRef

Camps-Valls, G., Bruzzone, L.,
Kernel-Based Methods for Hyperspectral Image Classification,
GeoRS(43), No. 6, June 2005, pp. 1351-1362.
IEEE Abstract. 0506
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
Elsevier DOI 0506
BibRef

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.
Elsevier DOI 0506
BibRef

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.
Elsevier DOI 0506
BibRef

Brown, A.J.,
Spectral Curve Fitting for Automatic Hyperspectral Data Analysis,
GeoRS(44), No. 6, June 2006, pp. 1601-1608.
IEEE DOI 0606
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Santurri, L.[Leonardo],
Aliasing assessment in wavelength domain of hyperspectral data,
RealTimeIP(1), No. 2, December 2006, pp. 131-141.
Springer DOI 0001
BibRef

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,
RealTimeIP(1), No. 2, December 2006, pp. 143-152.
Springer DOI 0001
BibRef

Kogan, J.[Jacob],
Introduction to Clustering Large and High-Dimensional Data,
Cambridge University Press2006. ISBN-13: 9780521852678
DOI Link Or:
WWW Link. Focused coverage of a few important algorithms. BibRef 0600

Hsu, P.H.[Pai-Hui],
Feature extraction of hyperspectral images using wavelet and matching pursuit,
PandRS(62), No. 2, June 2007, pp. 78-92.
Elsevier DOI 0709
Hyperspectral remote sensing; Wavelet transform; Feature extraction; Matching pursuit; Classification BibRef

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
BibRef

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
BibRef

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
BibRef

Bali, N., Mohammad-Djafari, A., Mohammadpoor, A.,
Joint Dimensionality Reduction, Classification and Segmentation of Hyperspectral Images,
ICIP06(969-972).
IEEE DOI 0610
BibRef

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
BibRef

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
BibRef

Orlov, N.[Nikita], Shamir, L.[Lior], Macura, T.[Tomasz], Johnston, J.[Josiah], Eckley, D.M.[D. Mark], Goldberg, I.G.[Ilya G.],
Wnd-charm: Multi-purpose image classification using compound image transforms,
PRL(29), No. 11, 1 August 2008, pp. 1684-1693.
Elsevier DOI 0804
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.
Elsevier DOI 0811
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,
RealTimeIP(7), No. 2, June 2012, pp. 105-129.
WWW Link. 1202
BibRef

Qiu, F.[Fang],
Neuro-fuzzy Based Analysis of Hyperspectral Imagery,
PhEngRS(74), No. 10, October 2008, pp. 1235-1248.
WWW Link. 0804
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.
WWW Link. 0804
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,
JOSA-A(25), No. 12, December 2008, pp. 3001-3012.
WWW Link. 0804
BibRef

Liu, X.W.[Xiu-Wen], Zhang, Q.A.[Qi-Ang],
Spectral histogram representations for visual modeling,
AIPR03(199-204).
IEEE DOI 0310
BibRef

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
BibRef

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

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

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

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
BibRef

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

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

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,
PIEEE(100), No. 3, March 2013, pp. 698-722.
IEEE DOI 1303
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
BibRef

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,
RS(6), No. 1, 2013, pp. 20-41.
DOI Link 1402
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

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

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
BibRef

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

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

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
BibRef
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.Y.[Chong-Yue], Gao, X.B.[Xin-Bo], Wang, Y.[Ying], Li, J.[Jie],
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

Xu, X.[Xiang], Li, J.[Jun], Li, S.T.[Shu-Tao],
Multiview Intensity-Based Active Learning for Hyperspectral Image Classification,
GeoRS(56), No. 2, February 2018, pp. 669-680.
IEEE DOI 1802
Feature extraction, Hyperspectral imaging, Learning systems, Prediction methods, Uncertainty, multiview intensity-based query (MVIQ) 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

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
BibRef

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

Chang, C.I.,
Spectral Inter-Band Discrimination Capacity of Hyperspectral Imagery,
GeoRS(56), No. 3, March 2018, pp. 1749-1766.
IEEE DOI 1804
hyperspectral imaging, image processing, probability, remote sensing, spectral analysis, BS -band subset, band selection, virtual dimensionality (VD) 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

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

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

Xia, J.S.[Jun-Shi], Ghamisi, P., Yokoya, N.[Naoto], Iwasaki, A.[Akira],
Random Forest Ensembles and Extended Multiextinction Profiles for Hyperspectral Image Classification,
GeoRS(56), No. 1, January 2018, pp. 202-216.
IEEE DOI 1801
Boosting, Feature extraction, Hyperspectral imaging, Radio frequency, Sensors, Vegetation, Ensemble learning, random forest (RF) BibRef

Li, L.[Lu], Wang, C.Y.[Cheng-Yi], Chen, J.B.[Jing-Bo], Ma, J.L.[Jiang-Lin],
Refinement of Hyperspectral Image Classification with Segment-Tree Filtering,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
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

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

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
BibRef
And: Corrections: GeoRS(56), No. 2, February 2018, pp. 1213-1213.
IEEE DOI 1802
Dictionaries, Encoding, Hyperspectral imaging, Image segmentation, Matching pursuit algorithms, Training, Dictionary learning, hyperspectral image (HSI) classification, segment, sparse, representation BibRef

Pan, B.[Bin], Shi, Z.W.[Zhen-Wei], Xu, X.[Xia],
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

Pan, B.[Bin], Shi, Z.W.[Zhen-Wei], Xu, X.[Xia], Yang, Y.[Yi],
Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
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

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

Hyperspectral imaging: defense technology transfers into commercial applications,
SPIE(Newsroom), October 9, 2017.
HTML Version. 1711
Photonics for a Better World. Hyperspectral imaging, like many other of today's technologies, is moving into numerous commercial markets after developing and maturing in the defense sector. BibRef

Peng, J.T.[Jiang-Tao], Du, Q.[Qian],
Robust Joint Sparse Representation Based on Maximum Correntropy Criterion for Hyperspectral Image Classification,
GeoRS(55), No. 12, December 2017, pp. 7152-7164.
IEEE DOI 1712
Adaptation models, Noise measurement, Optimization, Robustness, Testing, Training, Correntropy, outlier BibRef

Yang, J.L.[Jun-Li], Jiang, Z.G.[Zhi-Guo], Hao, S.[Shuang], Zhang, H.P.[Hao-Peng],
Higher Order Support Vector Random Fields for Hyperspectral Image Classification,
IJGI(7), No. 1, 2018, pp. xx-yy.
DOI Link 1801
BibRef

Liu, Q.S.[Qing-Shan], Zhou, F.[Feng], Hang, R.[Renlong], Yuan, X.T.[Xiao-Tong],
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Zhong, Z.L.[Zi-Long], Li, J.[Jonathan], Luo, Z.M.[Zhi-Ming], Chapman, M.[Michael],
Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework,
GeoRS(56), No. 2, February 2018, pp. 847-858.
IEEE DOI 1802
Feature extraction, Hyperspectral imaging, Machine learning, Robustness, Testing, Training, 3-D deep learning, spectral-spatial residual network (SSRN) BibRef

Zhang, X., Gao, Z., Jiao, L., Zhou, H.,
Multifeature Hyperspectral Image Classification With Local and Nonlocal Spatial Information via Markov Random Field in Semantic Space,
GeoRS(56), No. 3, March 2018, pp. 1409-1424.
IEEE DOI 1804
Markov processes, feature extraction, hyperspectral imaging, image classification, image segmentation, semantic representation BibRef

Cao, X., Zhou, F., Xu, L., Meng, D., Xu, Z., Paisley, J.,
Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network,
IP(27), No. 5, May 2018, pp. 2354-2367.
IEEE DOI 1804
Bayes methods, Markov processes, convolution, feedforward neural nets, gradient methods, hyperspectral imaging, convolutional neural networks BibRef

Hao, S., Wang, W., Ye, Y., Nie, T., Bruzzone, L.,
Two-Stream Deep Architecture for Hyperspectral Image Classification,
GeoRS(56), No. 4, April 2018, pp. 2349-2361.
IEEE DOI 1804
Feature extraction, Hyperspectral imaging, Machine learning, Training, Class-specific fusion, two-stream architecture BibRef

Hao, S., Wang, W., Ye, Y., Li, E., Bruzzone, L.,
A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss,
GeoRS(56), No. 8, August 2018, pp. 4650-4663.
IEEE DOI 1808
feature extraction, geophysical image processing, hyperspectral imaging, image classification, image resolution, super-resolution (SR) BibRef

Liu, B., Yu, X., Zhang, P., Yu, A., Fu, Q., Wei, X.,
Supervised Deep Feature Extraction for Hyperspectral Image Classification,
GeoRS(56), No. 4, April 2018, pp. 1909-1921.
IEEE DOI 1804
Euclidean distance, Feature extraction, Hyperspectral imaging, Support vector machines, Training, support vector machine (SVM) BibRef

Li, J.J.[Jiao-Jiao], Xi, B.[Bobo], Li, Y.S.[Yun-Song], Du, Q.[Qian], Wang, K.[Keyan],
Hyperspectral Classification Based on Texture Feature Enhancement and Deep Belief Networks,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Li, J.J.[Jiao-Jiao], Du, Q.[Qian], Li, Y.S.[Yun-Song], Li, W.[Wei],
Hyperspectral Image Classification With Imbalanced Data Based on Orthogonal Complement Subspace Projection,
GeoRS(56), No. 7, July 2018, pp. 3838-3851.
IEEE DOI 1807
Collaboration, Feature extraction, Hyperspectral imaging, Support vector machines, Testing, Training, sparse representation BibRef

Ge, H.[Haimiao], Wang, L.[Liguo], Liu, Y.[Yanzhong], Li, C.[Cheng], Chen, R.[Ruixin],
Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs,
IET-IPR(12), No. 6, June 2018, pp. 941-947.
DOI Link 1805
BibRef

Song, W., Li, S., Fang, L., Lu, T.,
Hyperspectral Image Classification With Deep Feature Fusion Network,
GeoRS(56), No. 6, June 2018, pp. 3173-3184.
IEEE DOI 1806
Convolutional neural networks, Feature extraction, Hyperspectral imaging, Logistics, Support vector machines, residual learning BibRef

Zu, B.K.[Bao-Kai], Xia, K.[Kewen], Du, W.[Wei], Li, Y.F.[Ya-Fang], Ali, A.[Ahmad], Chakraborty, S.[Sagnik],
Classification of Hyperspectral Images with Robust Regularized Block Low-Rank Discriminant Analysis,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Chen, M.[Mulin], Wang, Q.[Qi], Li, X.L.[Xue-Long],
Discriminant Analysis with Graph Learning for Hyperspectral Image Classification,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Samat, A.[Alim], Gamba, P.[Paolo], Liu, S.[Sicong], Li, E.[Erzhu], Miao, Z.[Zelang], Abuduwaili, J.[Jilili],
Fuzzy multiclass active learning for hyperspectral image classification,
IET-IPR(12), No. 7, July 2018, pp. 1095-1101.
DOI Link 1806
BibRef

Ma, X.R.[Xiao-Rui], Fu, A.[Anyan], Wang, J.[Jie], Wang, H.Y.[Hong-Yu], Yin, B.C.[Bao-Cai],
Hyperspectral Image Classification Based on Deep Deconvolution Network With Skip Architecture,
GeoRS(56), No. 8, August 2018, pp. 4781-4791.
IEEE DOI 1808
feedforward neural nets, geophysical image processing, image classification, image representation, hyperspectral image classification BibRef


Akbari, D.,
A New Spectral-spatial Framework for Classification of Hyperspectral Data,
GeoDisast17(7-10).
DOI Link 1805
BibRef

Huang, S., Zhang, H., Liao, W., Pižurica, A.,
Robust joint sparsity model for hyperspectral image classification,
ICIP17(3130-3134)
IEEE DOI 1803
Gaussian noise, Hyperspectral imaging, Optimization, Robustness, Sparse matrices, Training, Robust classification, super-pixel segmentation BibRef

Zhong, Z., Fan, B., Bai, J., Xiang, S., Pan, C.,
Structured binary feature extraction for hyperspectral imagery classification,
ICIP17(525-529)
IEEE DOI 1803
Binary codes, Dimensionality reduction, Feature extraction, Hyperspectral imaging, Synchronous digital hierarchy, Training, structured regularization 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

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

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
BibRef

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

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

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
BibRef

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

Akbari, D., Safari, A.R.,
Rule-Based Classification of a Hyperspectral Image Using MSSC Hierarchical Segmentation,
SMPR13(13-18).
HTML Version. 1311
BibRef

Niazmardi, S., Safari, A., Homayouni, S.,
Maximum Margin Clustering of Hyperspectral Data,
SMPR13(305-308).
HTML Version. 1311
BibRef

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

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
BibRef

Chisense, C.,
Classification of Roof Materials Using Hyperspectral Data,
ISPRS12(XXXIX-B7:103-107).
DOI Link 1209
BibRef

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
BibRef

Hasani, H.[Hadiseh],
Sensitivity analysis of support vector machine in classification of hyperspectral imagery,
CGC10(187).
PDF File. 1006
BibRef

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).
PDF File. 1006
BibRef

Nielsen, A.A.[Allan Aasbjerg],
Kernel methods in orthogonalization of multi-and hypervariate data,
ICIP09(3729-3732).
IEEE DOI 0911
BibRef

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
BibRef

Ramanath, R., Snyder, W.E., Qi, H.R.[Hai-Rong],
Eigenviews for object recognition in multispectral imaging systems,
AIPR03(33-38).
IEEE DOI 0310
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
BibRef

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
BibRef

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
BibRef

Gupta, M.R., Jacobson, N.P.,
Wavelet Principal Component Analysis and its Application to Hyperspectral Images,
ICIP06(1585-1588).
IEEE DOI 0610
BibRef

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
BibRef

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
BibRef

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
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

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
BibRef

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
BibRef

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
Spectral-Spatial Classification, Hyperspectral Data .


Last update:Aug 16, 2018 at 18:22:30