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
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
Pasolli, E.,
Melgani, F.,
Tuia, D.,
Pacifici, F.,
Emery, W.J.,
SVM Active Learning Approach for Image Classification Using Spatial
Information,
GeoRS(52), No. 4, April 2014, pp. 2217-2233.
IEEE DOI
1403
entropy
BibRef
Zhang, Z.[Zhou],
Pasolli, E.[Edoardo],
Crawford, M.M.[Melba M.],
An Adaptive Multiview Active Learning Approach for Spectral-Spatial
Classification of Hyperspectral Images,
GeoRS(58), No. 4, April 2020, pp. 2557-2570.
IEEE DOI
2004
Active learning (AL), classification, dynamic view,
hyperspectral data, multiview (MV), spatial features
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.[Gustavo],
Marsheva, T.V.B.[Tatyana V. Bandos],
Zhou, D.Y.[Deng-Yong],
Semi-Supervised Graph-Based Hyperspectral Image Classification,
GeoRS(45), No. 10, October 2007, pp. 3044-3054.
IEEE DOI
0711
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.[Asbjørn],
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
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
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
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
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
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
Li, C.[Cong],
Gao, L.[Lianru],
Plaza, A.[Antonio],
Zhang, B.[Bing],
FPGA implementation of a maximum simplex volume algorithm for endmember
extraction from remotely sensed hyperspectral images,
RealTimeIP(16), No. 5, October 2019, pp. 1681-1694.
WWW Link.
1911
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
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
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.W.[Li-Wei],
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
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, 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
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
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.,
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
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
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
Wang, Z.M.[Zeng-Mao],
Du, B.[Bo],
Zhang, L.F.[Le-Fei],
Zhang, L.P.[Liang-Pei],
Jia, X.P.[Xiu-Ping],
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
Yang, J.Q.[Jia-Qi],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
From center to surrounding: An interactive learning framework for
hyperspectral image classification,
PandRS(197), 2023, pp. 145-166.
Elsevier DOI
2303
Hyperspectral image classification, Deep learning,
Center-to-surrounding interactive learning, Transformer
BibRef
Yang, J.Q.[Jia-Qi],
Du, B.[Bo],
Wang, D.[Di],
Zhang, L.P.[Liang-Pei],
ITER: Image-to-Pixel Representation for Weakly Supervised HSI
Classification,
IP(33), 2024, pp. 257-272.
IEEE DOI
2312
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
Qv, H.,
Yin, J.,
Luo, X.,
Jia, X.,
Band Dual Density Discrimination Analysis for Hyperspectral Image
Classification,
GeoRS(56), No. 12, December 2018, pp. 7257-7271.
IEEE DOI
1812
Hyperspectral imaging, Indexes, Task analysis, Correlation,
Dimensionality reduction, Feature extraction,
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
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.J.[Heng-Jian],
Jiang, J.J.[Jun-Jun],
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
Leng, Q.M.[Qing-Ming],
Yang, H.[Haiou],
Jiang, J.J.[Jun-Jun],
Tian, Q.[Qi],
Adaptive MultiScale Segmentations for Hyperspectral Image
Classification,
GeoRS(58), No. 8, August 2020, pp. 5847-5860.
IEEE DOI
2007
Indexes, Image segmentation, Hyperspectral imaging,
Complexity theory, Image color analysis, Support vector machines,
multiscale segmentations
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
Yang, J.L.[Jun-Li],
Jiang, Z.G.[Zhi-Guo],
Hao, S.A.[Shu-Ang],
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
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
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.M.[Hai-Miao],
Wang, L.G.[Li-Guo],
Liu, Y.Z.[Yan-Zhong],
Li, C.[Cheng],
Chen, R.X.[Rui-Xin],
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
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
Samat, A.[Alim],
Gamba, P.[Paolo],
Liu, S.C.[Si-Cong],
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
Rocha, A.D.[Alby D.],
Groen, T.A.[Thomas A.],
Skidmore, A.K.[Andrew K.],
Darvishzadeh, R.[Roshanak],
Willemen, L.[Louise],
Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant
Traits Under Spatial Dependency,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link
1809
BibRef
Gao, H.M.[Hong-Min],
Yang, Y.[Yao],
Li, C.M.[Chen-Ming],
Zhou, H.[Hui],
Qu, X.Y.[Xiao-Yu],
Joint Alternate Small Convolution and Feature Reuse for Hyperspectral
Image Classification,
IJGI(7), No. 9, 2018, pp. xx-yy.
DOI Link
1810
BibRef
Makantasis, K.,
Doulamis, A.D.,
Doulamis, N.D.,
Nikitakis, A.,
Tensor-Based Classification Models for Hyperspectral Data Analysis,
GeoRS(56), No. 12, December 2018, pp. 6884-6898.
IEEE DOI
1812
Tensile stress, Hyperspectral imaging, Data models,
Machine learning, Algebra, Analytical models,
tensor-based classification
See also Data-Driven Background Subtraction Algorithm for In-Camera Acceleration in Thermal Imagery.
BibRef
Tzortzis, I.N.[Ioannis N.],
Rallis, I.[Ioannis],
Makantasis, K.[Konstantinos],
Doulamis, A.D.[Anastasios D.],
Doulamis, N.D.[Nikolaos D.],
Voulodimos, A.[Athanasios],
Automatic Inspection of Cultural Monuments Using Deep and
Tensor-Based Learning on Hyperspectral Imagery,
ICIP22(3136-3140)
IEEE DOI
2211
Deep learning, Training, Integrated optics, Inspection,
Optical imaging, Robustness, Optical materials,
Classification
BibRef
Makantasis, K.,
Voulodimos, A.,
Doulamis, A.D.,
Doulamis, N.D.,
Georgoulas, I.,
Hyperspectral Image Classification with Tensor-Based Rank-R Learning
Models,
ICIP19(3148-3125)
IEEE DOI
1910
Hyperspectral image classification, machine learning, tensor rank decomposition
BibRef
Li, S.,
Hao, Q.,
Gao, G.,
Kang, X.,
The Effect of Ground Truth on Performance Evaluation of Hyperspectral
Image Classification,
GeoRS(56), No. 12, December 2018, pp. 7195-7206.
IEEE DOI
1812
Hyperspectral imaging, Indexes, Performance evaluation,
Image color analysis, Correlation, Accuracy indexes,
performance evaluation
BibRef
Zhai, H.[Han],
Zhang, H.Y.[Hong-Yan],
Zhang, L.P.[Liang-Pei],
Li, P.X.[Ping-Xiang],
Total Variation Regularized Collaborative Representation Clustering
With a Locally Adaptive Dictionary for Hyperspectral Imagery,
GeoRS(57), No. 1, January 2019, pp. 166-180.
IEEE DOI
1901
Collaboration, Dictionaries, Clustering algorithms,
Clustering methods, Hyperspectral imaging, Sparse matrices,
total variation (TV)
BibRef
Pan, B.,
Shi, Z.,
Xu, X.,
Multiobjective-Based Sparse Representation Classifier for
Hyperspectral Imagery Using Limited Samples,
GeoRS(57), No. 1, January 2019, pp. 239-249.
IEEE DOI
1901
Feature extraction, Dictionaries, Hyperspectral imaging, Training,
Linear programming, Support vector machines,
sparse representation
BibRef
Jiang, J.,
Ma, J.,
Wang, Z.,
Chen, C.,
Liu, X.,
Hyperspectral Image Classification in the Presence of Noisy Labels,
GeoRS(57), No. 2, February 2019, pp. 851-865.
IEEE DOI
1901
Hyperspectral imaging, Noise measurement, Training, Databases,
Noise level, Radio frequency, Hyperspectral image classification,
superpixel segmentation
BibRef
Huang, N.[Nan],
Xiao, L.[Liang],
Hyperspectral image clustering via sparse dictionary-based anchored
regression,
IET-IPR(13), No. 2, February 2019, pp. 261-269.
DOI Link
1902
BibRef
Ma, X.,
Liu, W.,
Li, S.,
Tao, D.,
Zhou, Y.,
Hypergraph p-Laplacian Regularization for Remotely Sensed Image
Recognition,
GeoRS(57), No. 3, March 2019, pp. 1585-1595.
IEEE DOI
1903
approximation theory, graph theory, image recognition,
regression analysis, remote sensing, statistical distributions,
p-Laplacian
BibRef
Chang, C.,
Statistical Detection Theory Approach to Hyperspectral Image
Classification,
GeoRS(57), No. 4, April 2019, pp. 2057-2074.
IEEE DOI
1904
Bayes methods, feature extraction, hyperspectral imaging,
image classification, matrix algebra,
precision rate (PR)
BibRef
Windrim, L.[Lloyd],
Ramakrishnan, R.[Rishi],
Melkumyan, A.[Arman],
Murphy, R.J.[Richard J.],
Chlingaryan, A.[Anna],
Unsupervised Feature-Learning for Hyperspectral Data with
Autoencoders,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link
1904
BibRef
Windrim, L.[Lloyd],
Melkumyan, A.[Arman],
Murphy, R.J.[Richard J.],
Chlingaryan, A.[Anna],
Nieto, J.,
Unsupervised feature learning for illumination robustness,
ICIP16(4453-4457)
IEEE DOI
1610
Atmospheric modeling
BibRef
Pan, C.[Chao],
Gao, X.B.[Xin-Bo],
Wang, Y.[Ying],
Li, J.[Jie],
Markov Random Fields Integrating Adaptive Interclass-Pair Penalty and
Spectral Similarity for Hyperspectral Image Classification,
GeoRS(57), No. 5, May 2019, pp. 2520-2534.
IEEE DOI
1905
geophysical image processing, graph theory,
hyperspectral imaging, image classification, image segmentation,
spectral similarity
BibRef
Pan, C.[Chao],
Jia, X.P.[Xiu-Ping],
Li, J.[Jie],
Gao, X.B.[Xin-Bo],
Adaptive Edge Preserving Maps in Markov Random Fields for
Hyperspectral Image Classification,
GeoRS(59), No. 10, October 2021, pp. 8568-8583.
IEEE DOI
2109
Feature extraction, Support vector machines, Labeling,
Image edge detection, Kernel, Image segmentation,
Markov random fields (MRFs)
BibRef
Blanes, I.[Ian],
Kiely, A.[Aaron],
Hernández-Cabronero, M.[Miguel],
Serra-Sagristà, J.[Joan],
Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2
Low-Complexity Lossless and Near-Lossless Multispectral and
Hyperspectral Image Compression Standard,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
BibRef
Zhou, P.C.[Pei-Cheng],
Han, J.W.[Jun-Wei],
Cheng, G.[Gong],
Zhang, B.C.[Bao-Chang],
Learning Compact and Discriminative Stacked Autoencoder for
Hyperspectral Image Classification,
GeoRS(57), No. 7, July 2019, pp. 4823-4833.
IEEE DOI
1907
Feature extraction, Training, Hyperspectral imaging, Neurons, Kernel,
Deep learning, Discriminative stacked autoencoder (DSAE),
local Fisher discriminative regularization
BibRef
Brugger, A.[Anna],
Behmann, J.[Jan],
Paulus, S.[Stefan],
Luigs, H.G.[Hans-Georg],
Kuska, M.T.[Matheus Thomas],
Schramowski, P.[Patrick],
Kersting, K.[Kristian],
Steiner, U.[Ulrike],
Mahlein, A.K.[Anne-Katrin],
Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link
1907
BibRef
Bazine, R.[Razika],
Wu, H.Y.[Hua-Yi],
Boukhechba, K.[Kamel],
Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral
Imagery Classification,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link
1907
BibRef
Li, S.,
Song, W.,
Fang, L.,
Chen, Y.,
Ghamisi, P.,
Benediktsson, J.A.,
Deep Learning for Hyperspectral Image Classification: An Overview,
GeoRS(57), No. 9, September 2019, pp. 6690-6709.
IEEE DOI
1909
Feature extraction, Deep learning, Hyperspectral imaging, Training,
Logistics, Classification, deep learning, feature extraction,
hyperspectral image (HSI)
BibRef
Liu, X.,
Wang, R.,
Cai, Z.,
Cai, Y.,
Yin, X.,
Deep Multigrained Cascade Forest for Hyperspectral Image
Classification,
GeoRS(57), No. 10, October 2019, pp. 8169-8183.
IEEE DOI
1910
geophysical image processing, hyperspectral imaging,
image classification, learning (artificial intelligence),
machine learning
BibRef
Huang, Z.[Zehua],
Chen, Q.[Qi],
Chen, Q.H.[Qi-Hao],
Liu, X.G.[Xiu-Guo],
He, H.[Hao],
A Novel Hyperspectral Image Simulation Method Based on Nonnegative
Matrix Factorization,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Cevikalp, H.[Hakan],
High-dimensional data clustering by using local affine/convex hulls,
PRL(128), 2019, pp. 427-432.
Elsevier DOI
1912
BibRef
Tu, B.[Bing],
Zhou, C.[Chengle],
Peng, J.[Jin],
He, W.[Wei],
Ou, X.F.[Xian-Feng],
Xu, Z.[Zhi],
Kernel Entropy Component Analysis-Based Robust Hyperspectral Image
Supervised Classification,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Bazine, R.[Razika],
Wu, H.Y.[Hua-Yi],
Boukhechba, K.[Kamel],
Spectral DWT Multilevel Decomposition with Spatial Filtering
Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery
Classification,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Xu, Q.[Qin],
Xiao, Y.[Yong],
Wang, D.Y.[Dong-Yue],
Luo, B.[Bin],
CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial
Attention for Hyperspectral Image Classification,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link
2001
BibRef
Riese, F.M.[Felix M.],
Keller, S.[Sina],
Hinz, S.[Stefan],
Supervised and Semi-Supervised Self-Organizing Maps for Regression
and Classification Focusing on Hyperspectral Data,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link
2001
BibRef
Zahidi, U.A.[Usman A.],
Yuen, P.W.T.[Peter W. T.],
Piper, J.[Jonathan],
Godfree, P.S.[Peter S.],
An End-to-End Hyperspectral Scene Simulator with Alternate Adjacency
Effect Models and Its Comparison with CameoSim,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link
2001
BibRef
He, J.[Ji],
Zhao, L.[Lina],
Yang, H.W.[Hong-Wei],
Zhang, M.M.[Meng-Meng],
Li, W.[Wei],
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional
Encoder Representation From Transformers,
GeoRS(58), No. 1, January 2020, pp. 165-178.
IEEE DOI
2001
Feature extraction, Bit error rate, Hyperspectral imaging, Shape,
Deep learning, Kernel, Deep learning, hyperspectral image,
pattern recognition
BibRef
Alcolea, A.[Adrián],
Paoletti, M.E.[Mercedes E.],
Haut, J.M.[Juan M.],
Resano, J.[Javier],
Plaza, A.[Antonio],
Inference in Supervised Spectral Classifiers for On-Board
Hyperspectral Imaging: An Overview,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
2002
BibRef
Cui, B.[Binge],
Cui, J.[Jiandi],
Lu, Y.[Yan],
Guo, N.N.[Nan-Nan],
Gong, M.[Maoguo],
A Sparse Representation-Based Sample Pseudo-Labeling Method for
Hyperspectral Image Classification,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Zhang, Y.X.[Yu-Xiang],
Li, W.[Wei],
Li, H.C.[Heng-Chao],
Tao, R.[Ran],
Du, Q.[Qian],
Discriminative Marginalized Least-Squares Regression for
Hyperspectral Image Classification,
GeoRS(58), No. 5, May 2020, pp. 3148-3161.
IEEE DOI
2005
Hyperspectral imaging, Training, Task analysis, Manifolds,
Face recognition, Indexes, Hyperspectral image,
regression-based classification
BibRef
Tu, B.,
Zhou, C.,
He, D.,
Huang, S.,
Plaza, A.,
Hyperspectral Classification With Noisy Label Detection via
Superpixel-to-Pixel Weighting Distance,
GeoRS(58), No. 6, June 2020, pp. 4116-4131.
IEEE DOI
2005
Density peak (DP) clustering, Gaussian weighting,
hyperspectral images (HSI), noisy labels,
support vector machines (SVMs)
BibRef
Fang, F.[Fang],
Qiu, L.[Lei],
Yuan, S.[Shenfang],
Adaptive core fusion-based density peak clustering for complex data
with arbitrary shapes and densities,
PR(107), 2020, pp. 107452.
Elsevier DOI
2008
Clustering, Density peak, Core fusion, Arbitrary shape, Arbitrary density
BibRef
Dou, P.[Peng],
Zeng, C.[Chao],
Hyperspectral Image Classification Using Feature Relations Map
Learning,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link
2009
BibRef
Yang, Z.,
Cao, F.,
Cheng, Y.,
Ling, W.K.,
Hu, R.,
Locality Regularized Robust-PCRC: A Novel Simultaneous Feature
Extraction and Classification Framework for Hyperspectral Images,
GeoRS(58), No. 12, December 2020, pp. 8567-8582.
IEEE DOI
2012
Feature extraction, Collaboration, Training, Testing, Robustness,
Hyperspectral imaging, Coordinate information (CI),
sparse representation
BibRef
Chen, P.[Peng],
Minimum class variance broad learning system for hyperspectral image
classification,
IET-IPR(14), No. 13, November 2020, pp. 3039-3045.
DOI Link
2012
BibRef
Zhao, Q.H.[Quan-Hua],
Jia, S.H.[Shu-Han],
Li, Y.[Yu],
Hyperspectral Remote Sensing Image Classification Based on Tighter
Random Projection with Minimal Intra-Class Variance Algorithm,
PR(111), 2021, pp. 107635.
Elsevier DOI
2012
Random projection, Dimensionality reduction, Image size,
Minimum distance classifier, Hyperspectral remote sensing image classification
BibRef
Jia, S.H.[Shu-Han],
Zhao, Q.H.[Quan-Hua],
Li, Y.[Yu],
Hyperspectral Remote Sensing Image Classification Based on
Partitioned Random Projection Algorithm,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Jia, S.H.[Shu-Han],
Zhao, Q.H.[Quan-Hua],
Wang, L.,
Li, Y.[Yu],
Random Projection Based Bias-corrected Fuzzy C-means Algorithm For
Hyperspectral Remote Sensing Image Segmentation,
ISPRS20(B3:435-439).
DOI Link
2012
BibRef
Xu, Y.,
Wu, Z.,
Chanussot, J.,
Wei, Z.,
Hyperspectral Computational Imaging via Collaborative Tucker3 Tensor
Decomposition,
CirSysVideo(31), No. 1, January 2021, pp. 98-111.
IEEE DOI
2101
Tensile stress, Image reconstruction, Imaging,
Hyperspectral imaging, Matrix decomposition, Dictionaries,
spectral quadratic variation
BibRef
Pan, L.[Lei],
He, C.X.[Cheng-Xun],
Xiang, Y.[Yang],
Sun, L.[Le],
Multiscale Adjacent Superpixel-Based Extended Multi-Attribute
Profiles Embedded Multiple Kernel Learning Method for Hyperspectral
Classification,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Ghasrodashti, E.K.[Elham Kordi],
Sharma, N.[Nabin],
Hyperspectral image classification using an extended Auto-Encoder
method,
SP:IC(92), 2021, pp. 116111.
Elsevier DOI
2101
Hyperspectral image, Image classification, Auto-Encoder
BibRef
He, Z.P.[Zi-Ping],
Xia, K.[Kewen],
Li, T.J.[Tie-Jun],
Zu, B.K.[Bao-Kai],
Yin, Z.X.[Zhi-Xian],
Zhang, J.N.[Jiang-Nan],
A Constrained Graph-Based Semi-Supervised Algorithm Combined with
Particle Cooperation and Competition for Hyperspectral Image
Classification,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Guo, H.F.[Hu-Feng],
Liu, W.Y.[Wen-Yi],
S3L: Spectrum Transformer for Self-Supervised Learning in
Hyperspectral Image Classification,
RS(16), No. 6, 2024, pp. 970.
DOI Link
2403
BibRef
Liu, G.C.[Gui-Chi],
Gao, L.[Lei],
Qi, L.[Lin],
Hyperspectral Image Classification via Multi-Feature-Based
Correlation Adaptive Representation,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Gao, L.[Lei],
Guo, Z.[Zheng],
Guan, L.[Ling],
A Distinct Discriminant Canonical Correlation Analysis Network based
Deep Information Quality Representation for Image Classification*,
ICPR21(7595-7600)
IEEE DOI
2105
Correlation, Databases, Face recognition, Feature extraction,
Classification algorithms, Data mining, Task analysis,
image classification
BibRef
Anand, R.,
Veni, S.,
Aravinth, J.,
Robust Classification Technique for Hyperspectral Images Based on
3D-Discrete Wavelet Transform,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Pan, E.[Erting],
Ma, Y.[Yong],
Fan, F.[Fan],
Mei, X.G.[Xiao-Guang],
Huang, J.[Jun],
Hyperspectral Image Classification across Different Datasets:
A Generalization to Unseen Categories,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Huang, B.X.[Bao-Xiang],
Ge, L.Y.[Lin-Yao],
Chen, G.[Ge],
Radenkovic, M.[Milena],
Wang, X.P.[Xiao-Peng],
Duan, J.M.[Jin-Ming],
Pan, Z.K.[Zhen-Kuan],
Nonlocal graph theory based transductive learning for hyperspectral
image classification,
PR(116), 2021, pp. 107967.
Elsevier DOI
2106
Transductive learning, Nonlocal graph, Label propagation, Variational method,
Hyperspectral image classification
BibRef
Danielsen, A.S.[Aksel S.],
Johansen, T.A.[Tor Arne],
Garrett, J.L.[Joseph L.],
Self-Organizing Maps for Clustering Hyperspectral Images On-Board a
CubeSat,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Shi, C.[Cheng],
Fang, L.[Li],
Lv, Z.Y.[Zhi-Yong],
Zhao, M.H.[Ming-Hua],
Explainable scale distillation for hyperspectral image classification,
PR(122), 2022, pp. 108316.
Elsevier DOI
2112
Hyperspectral image classification, Knowledge distillation,
Scale distillation, Explainable scale network
BibRef
Alameddine, J.[Jihan],
Chehdi, K.[Kacem],
Cariou, C.[Claude],
Hierarchical Unsupervised Partitioning of Large Size Data and Its
Application to Hyperspectral Images,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Zhang, Y.I.[Youq-Iang],
Cao, G.[Guo],
Wang, B.S.[Bi-Sheng],
Li, X.S.[Xue-Song],
Amoako, P.Y.O.[Prince Yaw Owusu],
Shafique, A.[Ayesha],
Dual Sparse Representation Graph-Based Copropagation for
Semisupervised Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI
2112
Collaboration, Hyperspectral imaging, Telecommunications,
Linear programming, Support vector machines, Mathematical model,
sparse representation (SR) graph
BibRef
Shah, C.[Chiranjibi],
Du, Q.[Qian],
Xu, Y.[Yan],
Enhanced TabNet: Attentive Interpretable Tabular Learning for
Hyperspectral Image Classification,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Qing, Y.H.[Yu-Hao],
Huang, Q.Z.[Quan-Zhen],
Feng, L.Y.[Liu-Yan],
Qi, Y.Y.[Yue-Yan],
Liu, W.Y.[Wen-Yi],
Multiscale Feature Fusion Network Incorporating 3D Self-Attention for
Hyperspectral Image Classification,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zheng, H.[Hui],
Cao, Y.Z.[Yi-Zhi],
Sun, M.[Min],
Guo, G.H.[Gui-Hai],
Meng, J.Z.[Jun-Zhen],
Guo, X.W.[Xin-Wei],
Jiang, Y.C.[Yan-Chi],
Mixed Structure with 3D Multi-Shortcut-Link Networks for
Hyperspectral Image Classification,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link
2203
BibRef
Chakraborty, S.[Saptarshi],
Das, S.[Swagatam],
Detecting Meaningful Clusters From High-Dimensional Data:
A Strongly Consistent Sparse Center-Based Clustering Approach,
PAMI(44), No. 6, June 2022, pp. 2894-2908.
IEEE DOI
2205
Clustering algorithms, Feature extraction, Clustering methods,
Optimization, Noise measurement, Minimization, Context modeling,
strong consistency
BibRef
Gong, N.[Na],
Zhang, C.L.[Chun-Lei],
Zhou, H.[Heng],
Zhang, K.[Kai],
Wu, Z.Y.[Zhong-Yuan],
Zhang, X.[Xin],
Classification of hyperspectral images via improved cycle-MLP,
IET-CV(16), No. 5, 2022, pp. 468-478.
DOI Link
2207
cycleMLP, classification of hyperspectral image, deep learning, driftFC
BibRef
Yao, W.[Wei],
Lian, C.[Cheng],
Bruzzone, L.[Lorenzo],
A CNN Ensemble Based on a Spectral Feature Refining Module for
Hyperspectral Image Classification,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link
2210
BibRef
Nguyen, T.S.[Tan-Sy],
Luong, M.[Marie],
Kaaniche, M.[Mounir],
Ngo, L.H.[Long H.],
Beghdadi, A.[Azeddine],
A novel multi-branch wavelet neural network for sparse representation
based object classification,
PR(135), 2023, pp. 109155.
Elsevier DOI
2212
Object classification, Sparse coding, Wavelet transform,
Neural networks, Multi-branch architecture
BibRef
Ngo, L.H.[Long H.],
Luong, M.[Marie],
Sirakov, N.M.[Nikolay M.],
Le-Tien, T.[Thuong],
Guerif, S.[Sebastien],
Viennet, E.[Emmanuel],
Sparse Representation Wavelet Based Classification,
ICIP18(2974-2978)
IEEE DOI
1809
Improve conventional Sparse Representation Classification.
Dictionaries, Training, Discrete wavelet transforms, Face,
Wavelet domain, low-pass subband
BibRef
Cao, C.H.[Chun-Hong],
Duan, H.X.[Hong-Xuan],
Gao, X.[Xieping],
Hyperspectral image classification based on three-dimensional
adaptive sampling and improved iterative shrinkage-threshold
algorithm,
JVCIR(90), 2023, pp. 103693.
Elsevier DOI
2301
Feature learning, Discriminative feature,
Three-dimensional adaptive sampling, Hyperspectral images classification
BibRef
Guan, J.[Junyi],
Li, S.[Sheng],
He, X.X.[Xiong-Xiong],
Zhu, J.H.[Jin-Hui],
Chen, J.J.[Jia-Jia],
Si, P.[Peng],
SMMP: A Stable-Membership-Based Auto-Tuning Multi-Peak Clustering
Algorithm,
PAMI(45), No. 5, May 2023, pp. 6307-6319.
IEEE DOI
2304
Clustering algorithms, Shape, Density measurement,
Clustering methods, Resource management, Periodic structures,
auto-tuning
BibRef
Xu, Z.[Zeyu],
Su, C.[Cheng],
Wang, S.[Shirou],
Zhang, X.C.[Xiao-Can],
Local and Global Spectral Features for Hyperspectral Image
Classification,
RS(15), No. 7, 2023, pp. 1803.
DOI Link
2304
BibRef
Huang, S.G.[Shao-Guang],
Zhang, H.Y.[Hong-Yan],
Zeng, H.J.[Hai-Jin],
Pižurica, A.[Aleksandra],
From Model-Based Optimization Algorithms to Deep Learning Models for
Clustering Hyperspectral Images,
RS(15), No. 11, 2023, pp. 2832.
DOI Link
2306
BibRef
Huang, S.G.[Shao-Guang],
Zhang, H.Y.[Hong-Yan],
Liao, W.,
Pižurica, A.[Aleksandra],
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
Chen, H.Y.[Hua-Yue],
Wang, T.T.[Ting-Ting],
Chen, T.[Tao],
Deng, W.[Wu],
Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and
Random Patch Network,
RS(15), No. 13, 2023, pp. 3402.
DOI Link
2307
BibRef
Xie, J.X.[Jia-Xing],
Hua, J.J.[Jia-Jun],
Chen, S.N.[Shao-Nan],
Wu, P.W.[Pei-Wen],
Gao, P.[Peng],
Sun, D.Z.[Dao-Zong],
Lyu, Z.D.[Zhen-Dong],
Lyu, S.L.[Shi-Lei],
Xue, X.Y.[Xiu-Yun],
Lu, J.Q.[Jian-Qiang],
HyperSFormer: A Transformer-Based End-to-End Hyperspectral Image
Classification Method for Crop Classification,
RS(15), No. 14, 2023, pp. 3491.
DOI Link
2307
BibRef
Yang, Z.[Zian],
Zheng, N.R.[Nai-Rong],
Wang, F.[Feng],
DSSFN: A Dual-Stream Self-Attention Fusion Network for Effective
Hyperspectral Image Classification,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Gong, J.F.[Jin-Fu],
Li, F.M.[Fan-Ming],
Wang, J.[Jian],
Yang, Z.Y.[Zheng-Ye],
Ding, X.Z.[Xue-Zhuan],
A Split-Frequency Filter Network for Hyperspectral Image
Classification,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Li, S.Y.[Shu-Ying],
Wang, S.W.[Shao-Wei],
Li, Q.[Qiang],
Joint Posterior Probability Active Learning for Hyperspectral Image
Classification,
RS(15), No. 16, 2023, pp. 3936.
DOI Link
2309
BibRef
Liu, B.[Bing],
Sun, Y.F.[Yi-Fan],
Yu, A.[Anzhu],
Xue, Z.X.[Zhi-Xiang],
Zuo, X.B.[Xi-Bing],
Hyperspectral Meets Optical Flow:
Spectral Flow Extraction for Hyperspectral Image Classification,
IP(32), 2023, pp. 5181-5196.
IEEE DOI
2310
Optical Floe.
BibRef
Zhang, P.[Ping],
Yu, H.Y.[Hai-Yang],
Li, P.[Pengao],
Wang, R.[Ruili],
TransHSI: A Hybrid CNN-Transformer Method for Disjoint Sample-Based
Hyperspectral Image Classification,
RS(15), No. 22, 2023, pp. 5331.
DOI Link
2311
BibRef
Cui, B.[Binge],
Wen, J.X.[Jia-Xiang],
Song, X.[Xiukai],
He, J.L.[Jian-Long],
MADANet: A Lightweight Hyperspectral Image Classification Network
with Multiscale Feature Aggregation and a Dual Attention Mechanism,
RS(15), No. 21, 2023, pp. 5222.
DOI Link
2311
BibRef
Ding, C.[Chen],
Li, X.[Xu],
Chen, J.Y.[Jing-Yi],
Xu, Y.Y.[Yao-Yang],
Zheng, M.M.[Meng-Meng],
Zhang, L.[Lei],
Hyperspectral Image Classification Promotion Using Dynamic
Convolution Based on Structural Re-Parameterization,
RS(15), No. 23, 2023, pp. 5561.
DOI Link
2312
BibRef
Yang, X.F.[Xiao-Fei],
Cao, W.J.[Wei-Jia],
Lu, Y.[Yao],
Zhou, Y.C.[Yi-Cong],
QTN: Quaternion Transformer Network for Hyperspectral Image
Classification,
CirSysVideo(33), No. 12, December 2023, pp. 7370-7384.
IEEE DOI
2312
BibRef
Fang, S.[Shaoyi],
Li, X.Y.[Xin-Yu],
Tian, S.[Shimao],
Chen, W.H.[Wei-Hao],
Zhang, E.[Erlei],
Multi-Level Feature Extraction Networks for Hyperspectral Image
Classification,
RS(16), No. 3, 2024, pp. 590.
DOI Link
2402
BibRef
Zhang, Z.Q.[Zhong-Qiang],
Gao, D.[Dahua],
Liu, D.H.[Dan-Hua],
Shi, G.M.[Guang-Ming],
Spectral-Spatial Domain Attention Network for Hyperspectral Image
Few-Shot Classification,
RS(16), No. 3, 2024, pp. 592.
DOI Link
2402
BibRef
Liu, Y.[Yan],
Wang, X.X.[Xi-Xi],
Jiang, B.[Bo],
Chen, L.[Lan],
Luo, B.[Bin],
SemanticFormer:
Hyperspectral image classification via semantic transformer,
PRL(179), 2024, pp. 1-8.
Elsevier DOI Code:
WWW Link.
2403
Hyperspectral image (HSI) classification, Transformer, Semantic token
BibRef
Zhan, Y.[Ying],
Hu, D.[Dan],
Yu, X.[Xianchuan],
Wang, Y.F.[Yu-Feng],
Hyperspectral Image Classification Based on Mutually Guided Image
Filtering,
RS(16), No. 5, 2024, pp. 870.
DOI Link
2403
BibRef
Liao, Q.M.[Qi-Ming],
Zhao, L.[Lin],
Luo, W.Q.[Wen-Qiang],
Li, X.P.[Xin-Ping],
Zhang, G.[Guoyun],
Joint negative-positive-learning based sample reweighting for
hyperspectral image classification with label noise,
PRL(183), 2024, pp. 98-103.
Elsevier DOI
2406
Hyperspectral image (HSI), Meta learning,
Joint positive and negative learning (JPNL), Label-noise
BibRef
Yang, C.L.[Chun-Lan],
Kong, Y.[Yi],
Wang, X.S.[Xue-Song],
Cheng, Y.[Yuhu],
Hyperspectral Image Classification Based on Adaptive Global-Local
Feature Fusion,
RS(16), No. 11, 2024, pp. 1918.
DOI Link
2406
BibRef
Feng, Z.X.[Zhi-Xi],
Tong, S.L.[Shi-Lin],
Yang, S.Y.[Shu-Yuan],
Zhang, X.Y.[Xin-Yu],
Jiao, L.C.[Li-Cheng],
Pseudo-Label-Assisted Subdomain Adaptation for Hyperspectral Image
Classification,
CirSysVideo(34), No. 6, June 2024, pp. 4729-4744.
IEEE DOI
2406
Training, Feature extraction, Task analysis, Hyperspectral imaging,
Adaptation models, Testing, Sensors, Pseudo-label,
hyperspectral image (HSI) classification
BibRef
Li, G.F.[Guang-Fei],
Gao, Q.X.[Quan-Xue],
Han, J.G.[Jun-Gong],
Gao, X.B.[Xin-Bo],
A Coarse-to-Fine Cell Division Approach for Hyperspectral Remote
Sensing Image Classification,
CirSysVideo(34), No. 6, June 2024, pp. 4928-4941.
IEEE DOI
2406
Training, Feature extraction, Target recognition, Data models,
Complexity theory, Semantics, Task analysis, HRSIs, classification,
class specificity distribution
BibRef
Zhang, M.[Meng],
Yang, Y.[Yi],
Zhang, S.[Sixian],
Mi, P.B.[Peng-Bo],
Han, D.Q.[De-Qiang],
Spectral-Spatial Center-Aware Bottleneck Transformer for
Hyperspectral Image Classification,
RS(16), No. 12, 2024, pp. 2152.
DOI Link
2406
BibRef
Chen, Z.J.[Zhi-Jie],
Chen, Y.[Yu],
Wang, Y.[Yuan],
Wang, X.Y.[Xiao-Yan],
Wang, X.S.[Xin-Sheng],
Xiang, Z.R.[Zhou-Ru],
DCFF-Net: Deep Context Feature Fusion Network for High-Precision
Classification of Hyperspectral Image,
RS(16), No. 16, 2024, pp. 3002.
DOI Link
2408
BibRef
Liu, Y.[Yi],
Jiang, S.[Shanjiao],
Liu, Y.J.[Yi-Jin],
Mu, C.H.[Cai-Hong],
Spatial Feature Enhancement and Attention-Guided Bidirectional
Sequential Spectral Feature Extraction for Hyperspectral Image
Classification,
RS(16), No. 17, 2024, pp. 3124.
DOI Link
2409
BibRef
Guo, W.Q.[Wen-Qi],
Xu, X.[Xu],
Xu, X.Q.[Xiao-Qiang],
Gao, S.C.[Shi-Chen],
Wu, Z.[Zibu],
Clustering Hyperspectral Imagery via Sparse Representation Features
of the Generalized Orthogonal Matching Pursuit,
RS(16), No. 17, 2024, pp. 3230.
DOI Link
2409
BibRef
Yang, X.F.[Xiao-Fei],
Luo, Y.X.[Yu-Xiong],
Zhang, Z.[Zhen],
Tang, D.[Dong],
Zhou, Z.[Zheng],
Tang, H.J.[Hao-Jin],
AMHFN: Aggregation Multi-Hierarchical Feature Network for
Hyperspectral Image Classification,
RS(16), No. 18, 2024, pp. 3412.
DOI Link
2410
BibRef
Li, J.J.[Jiao-Jiao],
Zhang, Z.Y.[Zhi-Yuan],
Liu, Y.Z.[Yu-Zhe],
Song, R.[Rui],
Li, Y.S.[Yun-Song],
Du, Q.[Qian],
SWFormer: Stochastic Windows Convolutional Transformer for Hybrid
Modality Hyperspectral Classification,
IP(33), 2024, pp. 5482-5495.
IEEE DOI
2410
Feature extraction, Transformers, Hyperspectral imaging,
Laser radar, Convolution, Data mining, hybrid modality classification
BibRef
Zhang, F.G.[Feng-Gan],
Yan, H.[Hao],
Zhao, J.W.[Jian-Wei],
Hu, H.J.[Hao-Jie],
Euler Kernel Mapping for Hyperspectral Image Clustering via
Self-Paced Learning,
RS(16), No. 21, 2024, pp. 4097.
DOI Link
2411
BibRef
Wang, H.Q.[Hong-Qiao],
Yu, G.Q.[Guo-Qing],
Cheng, J.[Jinyu],
Zhang, Z.X.[Zhao-Xiang],
Wang, X.[Xuan],
Xu, Y.L.[Yue-Lei],
Fast Hyperspectral Image Classification with Strong Noise Robustness
Based on Minimum Noise Fraction,
RS(16), No. 20, 2024, pp. 3782.
DOI Link
2411
BibRef
Jamali, A.[Ali],
Roy, S.K.[Swalpa Kumar],
Hong, D.F.[Dan-Feng],
Lu, B.[Bing],
Ghamisi, P.[Pedram],
How to Learn More? Exploring Kolmogorov-Arnold Networks for
Hyperspectral Image Classification,
RS(16), No. 21, 2024, pp. 4015.
DOI Link
2411
BibRef
Reyes-Angulo, A.A.[Abel A.],
Paheding, S.[Sidike],
Forward-Forward Algorithm for Hyperspectral Image Classification,
PBVS24(3153-3161)
IEEE DOI
2410
Backpropagation, Training, Sensitivity, Feature extraction,
Classification algorithms, Hyperspectral Image Classification
BibRef
Zhao, H.[Hengwei],
Wang, X.Y.[Xin-Yu],
Li, J.T.[Jing-Tao],
Zhong, Y.F.[Yan-Fei],
Class Prior-Free Positive-Unlabeled Learning with Taylor Variational
Loss for Hyperspectral Remote Sensing Imagery,
ICCV23(16781-16790)
IEEE DOI Code:
WWW Link.
2401
BibRef
Shi, C.[Cheng],
Liu, Y.[Ying],
Zhao, M.H.[Ming-Hua],
You, Z.Z.[Zhen-Zhen],
Zhao, Z.Y.[Zi-Yuan],
Adversarial Defense via Perturbation-Disentanglement in Hyperspectral
Image Classification,
ICIP23(2935-2939)
IEEE DOI
2312
BibRef
Paheding, S.[Sidike],
Reyes, A.A.[Abel A.],
Kasaragod, A.[Anush],
Oommen, T.[Thomas],
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net
for Pixel-Wise Hyperspectral Image Classification,
PBVS22(408-416)
IEEE DOI
2210
Training, Deep learning, Transforms, Logic gates,
Object recognition, Task analysis
BibRef
Assainova, O.[Olga],
Rouot, J.[Jérémy],
Sedgh-Gooya, E.[Ehsan],
Taming the curse of dimensionality for perturbed token identification,
IPTA20(1-6)
IEEE DOI
2206
Filtering, Scalability, Image processing, Tools,
Nearest neighbor methods, Probabilistic logic, Tokenization,
Nearest neighbor search
BibRef
Wang, T.H.[Ting-Huai],
Wang, G.M.[Guang-Ming],
Tan, K.E.[Kuan Eeik],
Tan, D.H.[Dong-Hui],
Hyperspectral Image Classification via Pyramid Graph Reasoning,
ISVC20(I:707-718).
Springer DOI
2103
BibRef
May, S.,
Recursive Hierarchical Clustering for Hyperspectral Images,
ISPRS20(B3:461-465).
DOI Link
2012
BibRef
Santiago, J.G.[J. González],
Schenkel, F.,
Gross, W.,
Middelmann, W.,
An Unsupervised Labeling Approach for Hyperspectral Image
Classification,
ISPRS20(B3:407-415).
DOI Link
2012
BibRef
Hallé, P.,
Le Moan, S.[Steven],
Cariou, C.,
Towards a completely blind classifier for hyperspectral images,
IVCNZ17(1-6)
IEEE DOI
1902
feature extraction, geophysical image processing,
image classification, pattern clustering,
overall accuracy
BibRef
Le Moan, S.[Steven],
Cariou, C.,
Parameter-Free Density Estimation for Hyperspectral Image Clustering,
IVCNZ18(1-6)
IEEE DOI
1902
Kernel, Bandwidth, Hyperspectral imaging,
Probability density function, Estimation, Frequency estimation,
Parameter-free
BibRef
Rathore, P.,
Bezdek, J.C.,
Kumar, D.,
Rajasegarar, S.,
Palaniswami, M.,
Approximate Cluster Heat Maps of Large High-Dimensional Data,
ICPR18(195-200)
IEEE DOI
1812
Clustering algorithms, Visualization, Approximation algorithms,
Partitioning algorithms, Big Data, Heating systems,
Approximate cluster heat maps
BibRef
Akbari, D.,
A New Spectral-spatial Framework for Classification of Hyperspectral
Data,
GeoDisast17(7-10).
DOI Link
1805
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
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
Pervez, W.,
Khan, S.A.,
Valiuddin,
Hyperspectral Hyperion Imagery Analysis and Its Application Using
Spectral Analysis,
PIA15(169-175).
DOI Link
1504
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
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
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
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
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
Hyperspectral Data, Neural Networks for Classification .