14.2.2.4 Hyperspectral Data, Dimensionality Reduction, Band Selection

Chapter Contents (Back)
Hyperspectral. Dimensionality Reduction. See also Number of Features, Dimensionality Reduction.

Bruzzone, L., Serpico, S.B.,
A technique for feature selection in multiclass problems,
JRS(21), No. 3, February 2000, pp. 549. 0002
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Bruzzone, L.,
An Approach to Feature Selection and Classification of Remote Sensing Images Based on the Bayes Rule for Minimum Cost,
GeoRS(38), No. 1, January 2000, pp. 429-438.
IEEE Top Reference. 0002
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Serpico, S.B., Bruzzone, L.,
A new search algorithm for feature selection in hyperspectral remote sensing images,
GeoRS(39), No. 7, July 2001, pp. 1360-1367.
IEEE Top Reference. 0108
BibRef

Bruce, L.M., Koger, C.H., Li, J.[Jiang],
Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,
GeoRS(40), No. 10, October 2002, pp. 2331-2338.
IEEE Top Reference. 0301
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Kaewpijit, S., Le Moigne, J., El-Ghazawi, T.,
Automatic reduction of hyperspectral imagery using wavelet spectral analysis,
GeoRS(41), No. 4, April 2003, pp. 863-871.
IEEE Abstract. 0307
BibRef

Plaza, A.[Antonio], Martinez, P.[Pablo], Perez, R.[Rosa], Plaza, J.[Javier],
A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles,
PR(37), No. 6, June 2004, pp. 1097-1116.
WWW Link. 0405
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Plaza, A.[Antonio], Martinez, P.[Pablo], Plaza, J.[Javier], Perez, R.[Rosa],
Dimensionality Reduction and Classification of Hyperspectral Image Data Using Sequences of Extended Morphological Transformations,
GeoRS(43), No. 3, March 2005, pp. 466-479.
IEEE Abstract. 0501
BibRef

Chang, C.I.[Chein-I], Du, Q.[Qian],
Estimation of number of spectrally distinct signal sources in hyperspectral imagery,
GeoRS(42), No. 3, March 2004, pp. 608-619.
IEEE Abstract. 0407
BibRef

Chang, C.I.[Chein-I], Wang, S.,
Constrained Band Selection for Hyperspectral Imagery,
GeoRS(44), No. 6, June 2006, pp. 1575-1585.
IEEE DOI 0606
BibRef

Chang, C.I.[Chein-I], Liu, K.H.[Keng-Hao],
Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery,
GeoRS(52), No. 4, April 2014, pp. 2002-2017.
IEEE DOI 1403
geophysical signal processing See also Band Subset Selection for Anomaly Detection in Hyperspectral Imagery. BibRef

Wang, J., Chang, C.I.,
Independent Component Analysis-Based Dimensionality Reduction With Applications in Hyperspectral Image Analysis,
GeoRS(44), No. 6, June 2006, pp. 1586-1600.
IEEE DOI 0606
See also Linear Spectral Random Mixture Analysis for Hyperspectral Imagery. See also Applications of Independent Component Analysis in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery. BibRef

Chang, C.I.[Chein-I], Jiao, X.L.[Xiao-Li], Wu, C.C.[Chao-Cheng], Du, E.Y., Chen, H.M.[Hsian-Min],
Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery,
GeoRS(49), No. 11, November 2011, pp. 4123-4137.
IEEE DOI 1112
BibRef

Martínez Sotoca, J.[José], Pla, F., Salvador Sánchez, J.,
Band Selection in Multispectral Images by Minimization of Dependent Information,
SMC-C(37), No. 2, March 2007, pp. 258-267.
IEEE DOI 0703
BibRef

Martínez Sotoca, J.[José], Pla, F.[Filiberto],
Hyperspectral Data Selection from Mutual Information Between Image Bands,
SSPR06(853-861).
Springer DOI 0608
BibRef

Martínez Sotoca, J.[José], Salvador Sánchez, J., Pla, F.,
Attribute relevance in multiclass data sets using the naive bayes rule,
ICPR04(III: 426-429).
IEEE DOI 0409
BibRef

Martínez Sotoca, J.[José], Pla, F., Klaren, A.C.,
Unsupervised band selection for multispectral images using information theory,
ICPR04(III: 510-513).
IEEE DOI 0409
BibRef

Martínez-Usó, A.[Adolfo], Pla, F.[Filiberto], Martínez Sotoca, J.[José], García-Sevilla, P.[Pedro],
Clustering-Based Hyperspectral Band Selection Using Information Measures,
GeoRS(45), No. 12, December 2007, pp. 4158-4171.
IEEE DOI 0711
BibRef
Earlier: A1, A2, A4, A3:
Automatic Band Selection in Multispectral Images Using Mutual Information-Based Clustering,
CIARP06(644-654).
Springer DOI 0611
BibRef
And: A1, A2, A3, A4:
Clustering-based multispectral band selection using mutual information,
ICPR06(II: 760-763).
IEEE DOI 0609
BibRef

Martinez Sotoca, J.[Jose], Pla, F.[Filiberto],
Supervised feature selection by clustering using conditional mutual information-based distances,
PR(43), No. 6, June 2010, pp. 2068-2081.
Elsevier DOI 1003
Supervised feature selection; Clustering; Conditional mutual information See also Comments on supervised feature selection by clustering using conditional mutual information-based distances. BibRef

Bandos, T.V., Bruzzone, L., Camps-Valls, G.,
Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis,
GeoRS(47), No. 3, March 2009, pp. 862-873.
IEEE DOI 0903
BibRef

Camps-Valls, G., Serrano-López, A.J., Gómez-Chova, L., Martín-Guerrero, J.D., Calpe-Maravilla, J., Moreno, J.,
Regularized RBF Networks for Hyperspectral Data Classification,
ICIAR04(II: 429-436).
Springer DOI 0409
BibRef

Zhong, Y., Zhang, L., Huang, B., Li, P.,
An Unsupervised Artificial Immune Classifier for Multi/Hyperspectral Remote Sensing Imagery,
GeoRS(44), No. 2, February 2006, pp. 420-431.
IEEE DOI 0602
BibRef

Jiao, H., Zhong, Y., Zhang, L.,
An Unsupervised Spectral Matching Classifier Based on Artificial DNA Computing for Hyperspectral Remote Sensing Imagery,
GeoRS(52), No. 8, August 2014, pp. 4524-4538.
IEEE DOI 1403
DNA BibRef

Ma, A., Zhong, Y., Zhao, B., Jiao, H., Zhang, L.,
Semisupervised Subspace-Based DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery,
GeoRS(54), No. 8, August 2016, pp. 4402-4418.
IEEE DOI 1608
geophysical image processing BibRef

Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Gong, J., Li, P.,
A Supervised Artificial Immune Classifier for Remote-Sensing Imagery,
GeoRS(45), No. 12, December 2007, pp. 3957-3966.
IEEE DOI 0711
BibRef

Jiao, H.Z.[Hong-Zan], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data,
GeoRS(50), No. 10, October 2012, pp. 4085-4104.
IEEE DOI 1210
BibRef

Wu, K.[Ke], Zhao, D.[Dong], Zhong, Y.F.[Yan-Fei], Du, Q.[Qian],
Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery,
RS(8), No. 8, 2016, pp. 645.
DOI Link 1609
BibRef

Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery,
GeoRS(50), No. 3, March 2012, pp. 894-909.
IEEE DOI 1203
See also Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution. BibRef

Zhang, L., Zhong, Y., Huang, B., Gong, J., Li, P.,
Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery,
GeoRS(45), No. 12, December 2007, pp. 4172-4186.
IEEE DOI 0711
BibRef

Zhang, L.[Lefei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng], Huang, X.[Xin],
On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification,
GeoRS(50), No. 3, March 2012, pp. 879-893.
IEEE DOI 1203
BibRef

Zhang, L.[Lefei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng], Huang, X.[Xin],
A modified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images,
PandRS(83), No. 1, 2013, pp. 30-39.
Elsevier DOI 1307
BibRef
Earlier:
A Modified Stochastic Neighbor Embedding For Combining Multiple Features For Remote Sensing Image Classification,
AnnalsPRS(I-3), No. 2012, pp. 395-398.
HTML Version. 1209
Hyperspectral image See also Hyperspectral Image Noise Reduction Based on Rank-1 Tensor Decomposition. BibRef

Zhang, L.[Lefei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng], Huang, X.[Xin],
Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral-Spatial Feature Extraction,
GeoRS(51), No. 1, January 2013, pp. 242-256.
IEEE DOI 1301
BibRef

Zhang, L.[Lefei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng], Huang, X.[Xin],
Sparse Transfer Manifold Embedding for Hyperspectral Target Detection,
GeoRS(52), No. 2, February 2014, pp. 1030-1043.
IEEE DOI 1402
embedded systems BibRef

Zhang, L.[Lefei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng], Huang, X.[Xin], Du, B.,
Hyperspectral Remote Sensing Image Subpixel Target Detection Based on Supervised Metric Learning,
GeoRS(52), No. 8, August 2014, pp. 4955-4965.
IEEE DOI 1403
Feature extraction BibRef

Dong, Y.[Yanni], Zhang, L.P.[Liang-Pei], Zhang, L.[Lefei], Du, B.[Bo],
Maximum margin metric learning based target detection for hyperspectral images,
PandRS(108), No. 1, 2015, pp. 138-150.
Elsevier DOI 1511
Target detection BibRef

Zhao, R.[Rui], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Zhang, L.[Lefei],
A robust background regression based score estimation algorithm for hyperspectral anomaly detection,
PandRS(122), No. 1, 2016, pp. 126-144.
Elsevier DOI 1612
Hyperspectral BibRef

Shi, Q.[Qian], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Domain Adaptation for Remote Sensing Image Classification: A Low-Rank Reconstruction and Instance Weighting Label Propagation Inspired Algorithm,
GeoRS(53), No. 10, October 2015, pp. 5677-5689.
IEEE DOI 1509
image classification BibRef

Zhang, Y.X.[Yu-Xiang], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
A Sparse Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Images,
GeoRS(53), No. 3, March 2015, pp. 1346-1354.
IEEE DOI 1412
greedy algorithms BibRef

Du, B.[Bo], Zhang, Y.X.[Yu-Xiang], Zhang, L.P.[Liang-Pei], Tao, D.,
Beyond the Sparsity-Based Target Detector: A Hybrid Sparsity and Statistics-Based Detector for Hyperspectral Images,
IP(25), No. 11, November 2016, pp. 5345-5357.
IEEE DOI 1610
Detectors BibRef

Zhang, Y.X.[Yu-Xiang], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Wang, S.,
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection,
GeoRS(54), No. 3, March 2016, pp. 1376-1389.
IEEE DOI 1603
Approximation methods BibRef

Du, B.[Bo], Zhang, M.F.[Meng-Fei], Zhang, L.F.[Le-Fei], Hu, R.M.[Rui-Min], Tao, D.C.[Da-Cheng],
PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images,
MultMed(19), No. 1, January 2017, pp. 67-79.
IEEE DOI 1612
Correlation See also Hyperspectral Image Noise Reduction Based on Rank-1 Tensor Decomposition. BibRef

Chen, G., Qian, S.E.,
Dimensionality reduction of hyperspectral imagery using improved locally linear embedding,
AppRS(1), 2007, pp. 013509. BibRef 0700

Chen, G., Qian, S.E.,
Evaluation and comparison of dimensionality reduction techniques and band selection,
CanRS(34), No. 1, 2008, pp. 26-36. BibRef 0800

Chen, G., Qian, S.E.,
Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis,
JRS(30), No. 18, 2009, pp. 4889-4895, 2009. BibRef 0900

Chen, G., Qian, S.E.,
Simultaneous dimensionality reduction and denoising of yperspectral imagery using bivariate wavelet shrinking and PCA,
CanRS(34), No. 5, 2008, pp. 447-454, 2008. BibRef 0800

Qian, S.E.[Shen-En],
Dimensionality reduction of multidimensional satellite imagery,
SPIE(Newsroom), March 21, 2011.
DOI Link 1103
Novel techniques can reduce dimensionality to derive better remote-sensing products. BibRef

Qian, S.E.[Shen-En],
Enhancing space-based signal-to-noise ratios without redesigning the satellite,
SPIE(Newsroom), January 5, 2011.
DOI Link 1101
A newly developed signal-processing technology based on wavelets can improve the performance of satellite sensors by up to a factor of two. BibRef

Chen, G., Qian, S.E.,
Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage,
GeoRS(49), No. 3, March 2011, pp. 973-980.
IEEE DOI 1103
BibRef

Qian, S.E., Chen, G.,
Enhancing Spatial Resolution of Hyperspectral Imagery Using Sensor's Intrinsic Keystone Distortion,
GeoRS(50), No. 12, December 2012, pp. 5033-5048.
IEEE DOI 1212
BibRef

Jimenez-Rodriguez, L.O., Arzuaga-Cruz, E., Velez-Reyes, M.,
Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data,
GeoRS(45), No. 2, February 2007, pp. 469-483.
IEEE DOI 0703
BibRef

Serpico, S.B., Moser, G.,
Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes,
GeoRS(45), No. 2, February 2007, pp. 484-495.
IEEE DOI 0703
BibRef

Vaiphasa, C.[Chaichoke], Skidmore, A.K.[Andrew K.], de Boer, W.F.[Willem F.], Vaiphasa, T.[Tanasak],
A hyperspectral band selector for plant species discrimination,
PandRS(62), No. 3, August 2007, pp. 225-235.
WWW Link. 0709
Artificial_Intelligence; Classification; Hyper spectral; Mangrove; Remote sensing; Vegetation BibRef

Wang, S., Chang, C.I.,
Variable-Number Variable-Band Selection for Feature Characterization in Hyperspectral Signatures,
GeoRS(45), No. 9, September 2007, pp. 2979-2992.
IEEE DOI 0710
BibRef

Ball, J.E., Bruce, L.M.,
Level Set Hyperspectral Image Classification Using Best Band Analysis,
GeoRS(45), No. 10, October 2007, pp. 3022-3027.
IEEE DOI 0711
BibRef

Guo, B.F.[Bao-Feng], Damper, R.I., Gunn, S.R.[Steve R.], Nelson, J.D.B.,
A fast separability-based feature-selection method for high-dimensional remotely sensed image classification,
PR(41), No. 5, May 2008, pp. 1670-1679.
WWW Link. 0711
Feature selection; Mutual information; Remote sensing; Hyperspectral image classification BibRef

Mojaradi, B., Abrishami-Moghaddam, H., Valadan Zoej, M.J., Duin, R.P.W.,
Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction,
GeoRS(47), No. 7, July 2009, pp. 2091-2105.
IEEE DOI 0906
BibRef

Gheyas, I.A.[Iffat A.], Smith, L.S.[Leslie S.],
Feature subset selection in large dimensionality domains,
PR(43), No. 1, January 2010, pp. 5-13.
Elsevier DOI 0909
Curse of dimensionality; Feature subset selection; High dimensionality; Dimensionality reduction BibRef

Ververidis, D.[Dimitrios], Kotropoulos, C.[Constantine],
Information Loss of the Mahalanobis Distance in High Dimensions: Application to Feature Selection,
PAMI(31), No. 12, December 2009, pp. 2275-2281.
IEEE DOI 0911
Measure information loss in high dimensions, use to change limits in classifier for use in feature selection. BibRef

Haindl, M.[Michal], Somol, P.[Petr], Ververidis, D.[Dimitrios], Kotropoulos, C.[Constantine],
Feature Selection Based on Mutual Correlation,
CIARP06(569-577).
Springer DOI 0611
BibRef

Yang, J.M.[Jinn-Min], Yu, P.T.[Pao-Ta], Kuo, B.C.[Bor-Chen],
A Nonparametric Feature Extraction and Its Application to Nearest Neighbor Classification for Hyperspectral Image Data,
GeoRS(48), No. 3, March 2010, pp. 1279-1293.
IEEE DOI 1003
BibRef

Yang, J.M.[Jinn-Min], Kuo, B.C.[Bor-Chen], Yu, P.T.[Pao-Ta], Chuang, C.H.,
A Dynamic Subspace Method for Hyperspectral Image Classification,
GeoRS(48), No. 7, July 2010, pp. 2840-2853.
IEEE DOI 1007
BibRef

Huang, H.Y., Kuo, B.C.,
Double Nearest Proportion Feature Extraction for Hyperspectral-Image Classification,
GeoRS(48), No. 11, November 2010, pp. 4034-4046.
IEEE DOI 1011
BibRef

Zhao, Y.Q., Zhang, L., Kong, S.G.,
Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification,
GeoRS(49), No. 2, February 2011, pp. 747-756.
IEEE DOI 1102
BibRef

Cheng, Q.A.[Qi-Ang], Zhou, H.B.[Hong-Bo], Cheng, J.[Jie],
The Fisher-Markov Selector: Fast Selecting Maximally Separable Feature Subset for Multiclass Classification with Applications to High-Dimensional Data,
PAMI(33), No. 6, June 2011, pp. 1217-1233.
IEEE DOI 1105
Select subset of features in high dimensional data. BibRef

Cheng, Q.A.[Qi-Ang], Zhou, H.B.[Hong-Bo], Cheng, J.[Jie], Li, H.,
A Minimax Framework for Classification with Applications to Images and High Dimensional Data,
PAMI(36), No. 11, November 2014, pp. 2117-2130.
IEEE DOI 1410
Face recognition BibRef

Farzam, M., Beheshti, S.,
Simultaneous Denoising and Intrinsic Order Selection in Hyperspectral Imaging,
GeoRS(49), No. 9, September 2011, pp. 3423-3436.
IEEE DOI 1109
BibRef

Shahbaba, M.[Mahdi], Beheshti, S.[Soosan],
Signature test as statistical testing in clustering,
SIViP(10), No. 7, October 2016, pp. 1343-1351.
WWW Link. 1609
BibRef

Bermejo, P.[Pablo], Gamez, J.A.[Jose A.], Puerta, J.M.[Jose M.],
A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets,
PRL(32), No. 5, 1 April 2011, pp. 701-711.
Elsevier DOI 1103
Feature selection; Classification; GRASP; Filter; Wrapper; High-dimensional datasets BibRef

Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.,
Hyperspectral Image Classification With Independent Component Discriminant Analysis,
GeoRS(49), No. 12, December 2011, pp. 4865-4876.
IEEE DOI 1201
BibRef

Villa, A., Chanussot, J., Benediktsson, J.A., Jutten, C., Dambreville, R.,
Unsupervised methods for the classification of hyperspectral images with low spatial resolution,
PR(46), No. 6, June 2013, pp. 1556-1568.
Elsevier DOI 1302
Unsupervised classification; Spatial resolution improvement; Hyperspectral data; Source separation; Spatial regularization BibRef

Li, G.Z.[Guo-Zheng], Zhao, R.W.[Rui-Wei], Qu, H.N.[Hai-Ni], You, M.Y.[Ming-Yu],
Model selection for partial least squares based dimension reduction,
PRL(33), No. 5, 1 April 2012, pp. 524-529.
Elsevier DOI 1202
Partial least squares; Dimension reduction; Model selection BibRef

Prasad, S.[Saurabh], Li, W.[Wei], Fowler, J.E.[James E.], Bruce, L.M.[Lori Mann],
Information Fusion in the Redundant-Wavelet-Transform Domain for Noise-Robust Hyperspectral Classification,
GeoRS(50), No. 9, September 2012, pp. 3474-3486.
IEEE DOI 1209
BibRef

Li, W., Prasad, S., Fowler, J.E.,
Classification and Reconstruction From Random Projections for Hyperspectral Imagery,
GeoRS(51), No. 2, February 2013, pp. 833-843.
IEEE DOI 1302
BibRef

Li, W., Prasad, S., Fowler, J.E.,
Decision Fusion in Kernel-Induced Spaces for Hyperspectral Image Classification,
GeoRS(52), No. 6, June 2014, pp. 3399-3411.
IEEE DOI 1403
Feature extraction BibRef

Chen, C.[Chen], Li, W.[Wei], Tramel, E.W., Fowler, J.E.,
Reconstruction of Hyperspectral Imagery From Random Projections Using Multihypothesis Prediction,
GeoRS(52), No. 1, January 2014, pp. 365-374.
IEEE DOI 1402
compressed sensing BibRef

Li, W.[Wei], Tramel, E.W., Prasad, S., Fowler, J.E.,
Nearest Regularized Subspace for Hyperspectral Classification,
GeoRS(52), No. 1, January 2014, pp. 477-489.
IEEE DOI 1402
geophysical image processing BibRef

Wang, G.T.[Guang-Tao], Song, Q.B.[Qin-Bao], Xu, B.[Baowen], Zhou, Y.M.[Yu-Ming],
Selecting feature subset for high dimensional data via the propositional FOIL rules,
PR(46), No. 1, January 2013, pp. 199-214.
Elsevier DOI 1209
Feature subset selection; Feature interaction; Propositional FOIL rule; Filter method BibRef

Bonev, B.[Boyan], Escolano, F.[Francisco], Giorgi, D.[Daniela], Biasotti, S.[Silvia],
Information-theoretic selection of high-dimensional spectral features for structural recognition,
CVIU(117), No. 3, March 2013, pp. 214-228.
Elsevier DOI 1302
Feature selection; Pattern classification; Information theory; Mutual information; Entropy; Structure; Spectral features BibRef

Nouaouria, N.[Nabila], Boukadoum, M.[Mounir], Proulx, R.[Robert],
Particle swarm classification: A survey and positioning,
PR(46), No. 7, July 2013, pp. 2028-2044.
Elsevier DOI 1303
Particle swarm optimization; Classification; High dimensional data sets; Mixed attribute data sets BibRef

Cannas, L.M.[Laura Maria], Dessì, N.[Nicoletta], Pes, B.[Barbara],
Assessing similarity of feature selection techniques in high-dimensional domains,
PRL(34), No. 12, 1 September 2013, pp. 1446-1453.
Elsevier DOI 1306
Feature selection; Similarity measures; High-dimensional data BibRef

Vinh, N.X.[Nguyen X.], Bailey, J.[James],
Comments on supervised feature selection by clustering using conditional mutual information-based distances,
PR(46), No. 4, April 2013, pp. 1220-1225.
Elsevier DOI 1301
Feature selection; Conditional mutual information; Mutual information properties; Clustering; Classification; Naive Bayes classifier See also Supervised feature selection by clustering using conditional mutual information-based distances. BibRef

Cawse-Nicholson, K., Damelin, S.B., Robin, A., Sears, M.,
Determining the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory,
IP(22), No. 4, April 2013, pp. 1301-1310.
IEEE DOI 1303
BibRef

Mahmood, A., Robin, A., Sears, M.,
Modified Residual Method for the Estimation of Noise in Hyperspectral Images,
GeoRS(55), No. 3, March 2017, pp. 1451-1460.
IEEE DOI 1703
Correlation BibRef

Jia, X.P.[Xiu-Ping], Kuo, B.C.[Bor-Chen], Crawford, M.M.,
Feature Mining for Hyperspectral Image Classification,
PIEEE(100), No. 3, March 2013, pp. 676-697.
IEEE DOI 1303
BibRef

Liao, L.[Liang], Zhang, Y.N.[Yan-Ning], Maybank, S.J.[Stephen John], Liu, Z.F.[Zhou-Feng],
Intrinsic dimension estimation via nearest constrained subspace classifier,
PR(47), No. 3, 2014, pp. 1485-1493.
Elsevier DOI 1312
Intrinsic dimension estimation BibRef

Liao, L.[Liang], Zhang, Y.[Yanning], Maybank, S.J.[Stephen John], Liu, Z.F.[Zhou-Feng], Liu, X.[Xin],
Image recognition via two-dimensional random projection and nearest constrained subspace,
JVCIR(25), No. 5, 2014, pp. 1187-1198.
Elsevier DOI 1406
Supervised image classification BibRef

Chang, C.I.[Chein-I], Xiong, W.[Wei], Wen, C.H.[Chia-Hsien],
A Theory of High-Order Statistics-Based Virtual Dimensionality for Hyperspectral Imagery,
GeoRS(52), No. 1, January 2014, pp. 188-208.
IEEE DOI 1402
eigenvalues and eigenfunctions BibRef

Demarchi, L.[Luca], Canters, F.[Frank], Cariou, C.[Claude], Licciardi, G.[Giorgio], Chan, J.C.W.[Jonathan Cheung-Wai],
Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping,
PandRS(87), No. 1, 2014, pp. 166-179.
Elsevier DOI 1402
Airborne high-resolution hyperspectral imagery BibRef

Priem, F.[Frederik], Canters, F.[Frank],
Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping,
RS(8), No. 10, 2016, pp. 787.
DOI Link 1609
BibRef

Feng, J.[Jie], Jiao, L.C.[Li-Cheng], Zhang, X.R.[Xiang-Rong], Sun, T.[Tao],
Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection,
GeoRS(52), No. 7, July 2014, pp. 4092-4105.
IEEE DOI 1403
Approximation methods BibRef

Feng, J.[Jie], Jiao, L.C.[Li-Cheng], Sun, T.[Tao], Liu, H., Zhang, X.R.[Xiang-Rong],
Multiple Kernel Learning Based on Discriminative Kernel Clustering for Hyperspectral Band Selection,
GeoRS(54), No. 11, November 2016, pp. 6516-6530.
IEEE DOI 1610
Complexity theory BibRef

Feng, J.[Jie], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Sun, T.[Tao], Zhang, X.R.[Xiang-Rong],
Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy,
GeoRS(53), No. 5, May 2015, pp. 2956-2969.
IEEE DOI 1502
approximation theory BibRef

Nakamura, R.Y.M., Garcia Fonseca, L.M., dos Santos, J.A.[Jefersson A.], da Silva Torres, R.[Ricardo], Yang, X.S.[Xin-She], Papa, J.P.[J. Papa],
Nature-Inspired Framework for Hyperspectral Band Selection,
GeoRS(52), No. 4, April 2014, pp. 2126-2137.
IEEE DOI 1403
geophysical image processing BibRef

Lunga, D., Prasad, S., Crawford, M.M., Ersoy, O.,
Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning,
SPMag(31), No. 1, January 2014, pp. 55-66.
IEEE DOI 1403
feature extraction BibRef

Ramirez, A., Arguello, H., Arce, G.R., Sadler, B.M.,
Spectral Image Classification From Optimal Coded-Aperture Compressive Measurements,
GeoRS(52), No. 6, June 2014, pp. 3299-3309.
IEEE DOI 1403
coded-aperture snapshot spectral imaging. Compressive spectral. BibRef

Ramirez, A., Arce, G.R., Sadler, B.M.,
Spectral Image Unmixing From Optimal Coded-Aperture Compressive Measurements,
GeoRS(53), No. 1, January 2015, pp. 405-415.
IEEE DOI 1410
compressed sensing BibRef

Ren, H.[Hsuan], Wang, Y.L.[Yung-Ling], Huang, M.Y.[Min-Yu], Chang, Y.L.[Yang-Lang], Kao, H.M.[Hung-Ming],
Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data,
RS(6), No. 3, 2014, pp. 2069-2083.
DOI Link 1404
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Zabalza, J.[Jaime], Ren, J.C.[Jin-Chang], Yang, M.Q.[Ming-Qiang], Zhang, Y.[Yi], Wang, J.[Jun], Marshall, S.[Stephen], Han, J.W.[Jun-Wei],
Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing,
PandRS(93), No. 1, 2014, pp. 112-122.
Elsevier DOI 1407
Folded Principal Component Analysis (F-PCA) BibRef

Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen], Cheng, G.[Gong], Guo, L.[Lei], Ren, J.C.[Jin-Chang],
Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning,
GeoRS(53), No. 6, June 2015, pp. 3325-3337.
IEEE DOI 1503
feature extraction. Combine low level features with higher level grouping features. BibRef

Cheng, G.[Gong], Han, J.[Junwei],
A survey on object detection in optical remote sensing images,
PandRS(117), No. 1, 2016, pp. 11-28.
Elsevier DOI 1605
Object detection BibRef

Cheng, G.[Gong], Zhou, P., Han, J.[Junwei],
Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images,
GeoRS(54), No. 12, December 2016, pp. 7405-7415.
IEEE DOI 1612
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And:
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection,
CVPR16(2884-2893)
IEEE DOI 1612
image processing BibRef

Lu, Q.[Qikai], Huang, X.[Xin], Zhang, L.P.[Liang-Pei],
A Novel Clustering-Based Feature Representation for the Classification of Hyperspectral Imagery,
RS(6), No. 6, 2014, pp. 5732-5753.
DOI Link 1407
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Cheng, K.J.[Kai-Jen], Dill, J.,
Lossless to Lossy Dual-Tree BEZW Compression for Hyperspectral Images,
GeoRS(52), No. 9, Sept 2014, pp. 5765-5770.
IEEE DOI 1407
data compression BibRef

Pu, H.[Hanye], Chen, Z.[Zhao], Wang, B.[Bin], Jiang, G.,
A Novel Spatial-Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery,
GeoRS(52), No. 11, November 2014, pp. 7008-7022.
IEEE DOI 1407
Geologic measurements See also Constrained Least Squares Algorithms for Nonlinear Unmixing of Hyperspectral Imagery. BibRef

Geng, X., Sun, K., Ji, L., Zhao, Y.,
A Fast Volume-Gradient-Based Band Selection Method for Hyperspectral Image,
GeoRS(52), No. 11, November 2014, pp. 7111-7119.
IEEE DOI 1407
Computational complexity BibRef

Ren, J., Zabalza, J., Marshall, S., Zheng, J.,
Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging,
SPMag(31), No. 4, July 2014, pp. 149-154.
IEEE DOI 1407
Applications Corner. Covariance matrices BibRef

Tang, Y.Y.[Yuan Yan], Yuan, H.L.[Hao-Liang], Li, L.Q.[Luo-Qing],
Manifold-Based Sparse Representation for Hyperspectral Image Classification,
GeoRS(52), No. 12, December 2014, pp. 7606-7618.
IEEE DOI 1410
geophysical image processing BibRef

Tang, Y.Y.[Yuan Yan], Lu, Y.[Yang], Yuan, H.L.[Hao-Liang],
Hyperspectral Image Classification Based on Three-Dimensional Scattering Wavelet Transform,
GeoRS(53), No. 5, May 2015, pp. 2467-2480.
IEEE DOI 1502
Gaussian processes BibRef

Luo, H.W.[Hui-Wu], Tang, Y.Y.[Yuan Yan], Wang, Y.L.[Yu-Long], Wang, J.Z.[Jian-Zhong], Yang, L.[Lina], Li, C.L.[Chun-Li], Hu, T.B.[Ting-Bo],
Hyperspectral Image Classification Based on Spectral-Spatial One-Dimensional Manifold Embedding,
GeoRS(54), No. 9, September 2016, pp. 5319-5340.
IEEE DOI 1609
feature extraction BibRef

Yuan, H., Tang, Y.Y.[Yuan Yan],
Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification,
Cyber(47), No. 4, April 2017, pp. 934-945.
IEEE DOI 1704
Computational modeling BibRef

Luo, H.W.[Hui-Wu], Wang, Y.L.[Yu-Long], Tang, Y.Y.[Yuan Yan], Li, C.L.[Chun-Li], Wang, J.Z.[Jian-Zhong],
Hyperspectral image classification using distance metric based 1-dimensional manifold embedding,
ICWAPR16(247-251)
IEEE DOI 1611
Hyperspectral imaging BibRef

Ye, Z.J.[Zhi-Jing], Li, H.[Hong], Song, Y.L.[Ya-Long], Benediktsson, J.A.[Jón Atli], Tang, Y.Y.[Yuan Yan],
Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation,
GeoRS(55), No. 2, February 2017, pp. 1199-1209.
IEEE DOI 1702
feature extraction BibRef

Yuan, H.L.[Hao-Liang], Tang, Y.Y.[Yuan Yan],
Multi-scale Tensor l1-Based Algorithm for Hyperspectral Image Classification,
ICPR14(1383-1388)
IEEE DOI 1412
Accuracy; Hyperspectral imaging; Tensile stress; Training; Vectors BibRef

Yuan, Y.[Yuan], Zhu, G.K.[Guo-Kang], Wang, Q.[Qi],
Hyperspectral Band Selection by Multitask Sparsity Pursuit,
GeoRS(53), No. 2, February 2015, pp. 631-644.
IEEE DOI 1411
data visualisation BibRef

Blanes, I., Hernandez-Cabronero, M., Auli-Llinas, F., Serra-Sagrista, J., Marcellin, M.W.,
Iso-range Pairwise Orthogonal Transform,
GeoRS(53), No. 6, June 2015, pp. 3361-3372.
IEEE DOI 1503
data compression BibRef

Guccione, P., Mascolo, L., Appice, A.,
Iterative Hyperspectral Image Classification Using Spectral-Spatial Relational Features,
GeoRS(53), No. 7, July 2015, pp. 3615-3627.
IEEE DOI 1503
Decoding BibRef

Imani, M.[Maryam], Ghassemian, H.[Hassan],
Feature space discriminant analysis for hyperspectral data feature reduction,
PandRS(102), No. 1, 2015, pp. 1-13.
Elsevier DOI 1503
Hyperspectral image BibRef

Imani, M.[Maryam], Ghassemian, H.[Hassan],
Edge patch image-based morphological profiles for classification of multispectral and hyperspectral data,
IET-IPR(11), No. 3, March 2017, pp. 164-172.
DOI Link 1703
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Zabalza, J., Ren, J.C.[Jin-Chang], Zheng, J.B.[Jiang-Bin], Han, J.W.[Jun-Wei], Zhao, H.M.[Hui-Min], Li, S.T.[Shu-Tao], Marshall, S.,
Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging,
GeoRS(53), No. 8, August 2015, pp. 4418-4433.
IEEE DOI 1506
feature extraction BibRef

Huang, H.[Hong], Yang, M.,
Dimensionality Reduction of Hyperspectral Images With Sparse Discriminant Embedding,
GeoRS(53), No. 9, September 2015, pp. 5160-5169.
IEEE DOI 1506
Eigenvalues and eigenfunctions BibRef

Huang, H.[Hong], Luo, F.[Fulin], Liu, J.[Jiamin], Yang, Y.Q.[Ya-Qiong],
Dimensionality reduction of hyperspectral images based on sparse discriminant manifold embedding,
PandRS(106), No. 1, 2015, pp. 42-54.
Elsevier DOI 1507
Hyperspectral image classification BibRef

Guan, L.X.[Li-Xin], Xie, W.X.[Wei-Xin], Pei, J.H.[Ji-Hong],
Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images,
PR(48), No. 10, 2015, pp. 3216-3226.
Elsevier DOI 1507
Feature extraction BibRef

Long, Y.[Yi], Li, H.C.[Heng-Chao], Celik, T.[Turgay], Longbotham, N.[Nathan], Emery, W.J.[William J.],
Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization,
RS(7), No. 6, 2015, pp. 7785.
DOI Link 1507
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Patra, S., Modi, P., Bruzzone, L.,
Hyperspectral Band Selection Based on Rough Set,
GeoRS(53), No. 10, October 2015, pp. 5495-5503.
IEEE DOI 1509
feature selection BibRef

Yang, S.Y.[Shu-Yuan], Wang, M.[Min], Li, P.[Peng], Jin, L.[Li], Wu, B.[Bin], Jiao, L.C.[Li-Cheng],
Compressive Hyperspectral Imaging via Sparse Tensor and Nonlinear Compressed Sensing,
GeoRS(53), No. 11, November 2015, pp. 5943-5957.
IEEE DOI 1509
compressed sensing BibRef

Sumarsono, A., Du, Q.[Qian],
Low-Rank Subspace Representation for Estimating the Number of Signal Subspaces in Hyperspectral Imagery,
GeoRS(53), No. 11, November 2015, pp. 6286-6292.
IEEE DOI 1509
geophysical image processing BibRef

Falco, N., Benediktsson, J.A., Bruzzone, L.,
Spectral and Spatial Classification of Hyperspectral Images Based on ICA and Reduced Morphological Attribute Profiles,
GeoRS(53), No. 11, November 2015, pp. 6223-6240.
IEEE DOI 1509
feature extraction BibRef

Gholizadeh, H.[Hamed], Mojaradi, B.[Barat], Zoej, M.J.V.[Mohammad Javad Valadan],
Local Prototype Space-based Band Selection for Hyperspectral Subpixel Analysis,
PFG(2015), No. 5, 2015, pp. 373-380.
DOI Link 1512
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Chen, Y.N.[Ying-Nong], Hsieh, C.T.[Cheng-Ta], Wen, M.G.[Ming-Gang], Han, C.C.[Chin-Chuan], Fan, K.C.[Kuo-Chin],
A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation,
RS(7), No. 11, 2015, pp. 14292.
DOI Link 1512
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Jia, S.[Sen], Tang, G.H.[Gui-Hua], Zhu, J.[Jiasong], Li, Q.Q.[Qing-Quan],
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection,
GeoRS(54), No. 1, January 2016, pp. 88-102.
IEEE DOI 1601
geophysical image processing BibRef

Zhu, G.K.[Guo-Kang], Huang, Y.C.[Yuan-Cheng], Lei, J.S.[Jing-Sheng], Bi, Z.Q.[Zhong-Qin], Xu, F.F.[Fei-Fei],
Unsupervised Hyperspectral Band Selection by Dominant Set Extraction,
GeoRS(54), No. 1, January 2016, pp. 227-239.
IEEE DOI 1601
benchmark testing BibRef

Gong, M.G.[Mao-Guo], Zhang, M.Y.[Ming-Yang], Yuan, Y.[Yuan],
Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images,
GeoRS(54), No. 1, January 2016, pp. 544-557.
IEEE DOI 1601
decision making BibRef

Hang, R., Liu, Q., Song, H., Sun, Y.,
Matrix-Based Discriminant Subspace Ensemble for Hyperspectral Image Spatial-Spectral Feature Fusion,
GeoRS(54), No. 2, February 2016, pp. 783-794.
IEEE DOI 1601
Feature extraction BibRef

Sun, W.W.[Wei-Wei], Jiang, M.[Man], Li, W.[Weiyue], Liu, Y.N.[Yin-Nian],
A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification,
RS(8), No. 3, 2016, pp. 238.
DOI Link 1604
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Peng, J.T.[Jiang-Tao], Luo, T.[Tao],
Sparse matrix transform-based linear discriminant analysis for hyperspectral image classification,
SIViP(10), No. 4, April 2016, pp. 761-768.
WWW Link. 1604
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Veganzones, M.A., Cohen, J.E., Cabral Farias, R., Chanussot, J., Comon, P.,
Nonnegative Tensor CP Decomposition of Hyperspectral Data,
GeoRS(54), No. 5, May 2016, pp. 2577-2588.
IEEE DOI 1604
hyperspectral imaging BibRef

Halimi, A., Honeine, P., Kharouf, M., Richard, C., Tourneret, J.Y.,
Estimating the Intrinsic Dimension of Hyperspectral Images Using a Noise-Whitened Eigengap Approach,
GeoRS(54), No. 7, July 2016, pp. 3811-3821.
IEEE DOI 1606
Correlation BibRef

Drumetz, L., Veganzones, M.A., Gómez, R.M.[R. Marrero], Tochon, G., Mura, M.D., Licciardi, G.A., Jutten, C., Chanussot, J.,
Hyperspectral Local Intrinsic Dimensionality,
GeoRS(54), No. 7, July 2016, pp. 4063-4078.
IEEE DOI 1606
Correlation BibRef

Xia, J.S.[Jun-Shi], Bombrun, L.[Lionel], Adali, T., Berthoumieu, Y.[Yannick], Germain, C.[Christian],
Spectral-Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy,
GeoRS(54), No. 8, August 2016, pp. 4971-4982.
IEEE DOI 1608
feature extraction BibRef

Xia, J.S.[Jun-Shi], Bombrun, L.[Lionel], Berthoumieu, Y.[Yannick], Germain, C.[Christian],
Multiple features learning via rotation strategy,
ICIP16(2206-2210)
IEEE DOI 1610
Feature extraction BibRef

Luo, F., Huang, H., Ma, Z., Liu, J.,
Semisupervised Sparse Manifold Discriminative Analysis for Feature Extraction of Hyperspectral Images,
GeoRS(54), No. 10, October 2016, pp. 6197-6211.
IEEE DOI 1610
feature extraction BibRef

Chen, Y.S.[Yu-Shi], Jiang, H.L.[Han-Lu], Li, C.Y.[Chun-Yang], Jia, X.P.[Xiu-Ping], Ghamisi, P.[Pedram],
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks,
GeoRS(54), No. 10, October 2016, pp. 6232-6251.
IEEE DOI 1610
feature extraction BibRef

Mou, L., Ghamisi, P.[Pedram], Zhu, X.X.,
Deep Recurrent Neural Networks for Hyperspectral Image Classification,
GeoRS(55), No. 7, July 2017, pp. 3639-3655.
IEEE DOI 1706
Data models, Hyperspectral imaging, Logic gates, Recurrent neural networks, Support vector machines, Convolutional neural network (CNN), deep learning, gated recurrent unit (GRU), hyperspectral image classification, long short-term memory (LSTM), recurrent, neural, network, (RNN) BibRef

He, Z.[Zhi], Li, J., Liu, L.[Lin], Liu, K., Zhuo, L.,
Fast Three-Dimensional Empirical Mode Decomposition of Hyperspectral Images for Class-Oriented Multitask Learning,
GeoRS(54), No. 11, November 2016, pp. 6625-6643.
IEEE DOI 1610
Data mining BibRef

He, Z.[Zhi], Liu, L.[Lin],
Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification,
PandRS(121), No. 1, 2016, pp. 11-27.
Elsevier DOI 1609
Hyperspectral image (HSI) BibRef

Li, S.J.[Shi-Jin], Zheng, Z.[Zhan], Wang, Y.[Yaming], Chang, C.[Chun], Yu, Y.[Yufeng],
A new hyperspectral band selection and classification framework based on combining multiple classifiers,
PRL(83, Part 2), No. 1, 2016, pp. 152-159.
Elsevier DOI 1609
Hyperspectral imaging BibRef

Rasti, B., Ulfarsson, M.O., Sveinsson, J.R.,
Hyperspectral Feature Extraction Using Total Variation Component Analysis,
GeoRS(54), No. 12, December 2016, pp. 6976-6985.
IEEE DOI 1612
feature extraction BibRef

Rasti, B., Ghamisi, P., Gloaguen, R.,
Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis,
GeoRS(55), No. 7, July 2017, pp. 3997-4007.
IEEE DOI 1706
Data mining, Feature extraction, Hyperspectral imaging, Laser radar, Support vector machines, Extinction profiles (EPs), feature fusion, orthogonal total variation component analysis (OTVCA), random forest (RF), support, vector, machines, (SVMs) BibRef

Bian, X.Y.[Xiao-Yong], Chen, C.[Chen], Xu, Y.[Yan], Du, Q.[Qian],
Robust Hyperspectral Image Classification by Multi-Layer Spatial-Spectral Sparse Representations,
RS(8), No. 12, 2016, pp. 985.
DOI Link 1612
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Allab, K.[Kais], Labiod, L.[Lazhar], Nadif, M.[Mohamed],
Multi-manifold matrix decomposition for data co-clustering,
PR(64), No. 1, 2017, pp. 386-398.
Elsevier DOI 1701
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And: Erratum: PR(69), No. 1, 2017, pp. 352-353.
Elsevier DOI 1706
Co-clustering BibRef

Ahlberg, J.,
Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery,
GeoRS(55), No. 2, February 2017, pp. 658-670.
IEEE DOI 1702
atmospheric humidity BibRef

Feng, S., Itoh, Y., Parente, M., Duarte, M.F.,
Hyperspectral Band Selection From Statistical Wavelet Models,
GeoRS(55), No. 4, April 2017, pp. 2111-2123.
IEEE DOI 1704
geophysical image processing BibRef

Damodaran, B.B., Courty, N., Lefèvre, S.,
Sparse Hilbert Schmidt Independence Criterion and Surrogate-Kernel-Based Feature Selection for Hyperspectral Image Classification,
GeoRS(55), No. 4, April 2017, pp. 2385-2398.
IEEE DOI 1704
Hilbert spaces BibRef

Kemker, R., Kanan, C.,
Self-Taught Feature Learning for Hyperspectral Image Classification,
GeoRS(55), No. 5, May 2017, pp. 2693-2705.
IEEE DOI 1705
geophysical image processing, hyperspectral imaging, image classification, independent component analysis, learning (artificial intelligence), HSI classification, Indian Pines, Pavia University data sets, Salinas Valley, deep supervised network, hyperspectral image classification, independent component analysis, machine learning problem, self-taught learning, supervised deep learning methods, three-layer stacked convolutional autoencoder, Data mining, Data models, Encoding, Feature extraction, Hyperspectral imaging, Sensors, Training, Autoencoder, deep learning, feature learning, hyperspectral imaging, independent component analysis (ICA), self-taught, learning BibRef

Dong, Y., Du, B., Zhang, L., Zhang, L.,
Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning,
GeoRS(55), No. 5, May 2017, pp. 2509-2524.
IEEE DOI 1705
hyperspectral imaging, image classification, remote sensing, EDLML algorithm, data dimensionality, distance metric learning, ensemble discriminative local metric learning, global metric learning, high-dimensional data space, hyperspectral dataset, hyperspectral image analysis, hyperspectral image classification, hyperspectral image dimensionality reduction, Algorithm design and analysis, Hyperspectral imaging, Learning systems, Measurement, Principal component analysis, Training, Dimensionality reduction, ensemble learning, hyperspectral image (HSI) classification, local, discriminative, distance, metrics BibRef

Sun, W., Yang, G., Du, B., Zhang, L., Zhang, L.,
A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification,
GeoRS(55), No. 7, July 2017, pp. 4032-4046.
IEEE DOI 1706
Feature extraction, Hyperspectral imaging, Learning systems, Manifolds, Principal component analysis, Sparse matrices, Classification, dimensionality reduction, feature extraction, hyperspectral imagery (HSI), sparse, and, low-rank, near-isometric, linear, embedding, (SLRNILE) BibRef

Yang, R.L.[Rong-Lu], Su, L.[Lifan], Zhao, X.[Xibin], Wan, H.[Hai], Sun, J.G.[Jia-Guang],
Representative band selection for hyperspectral image classification,
JVCIR(48), No. 1, 2017, pp. 396-403.
Elsevier DOI 1708
High, dimensional, image BibRef

Luo, F.[Fulin], Huang, H.[Hong], Duan, Y.[Yule], Liu, J.[Jiamin], Liao, Y.H.[Ying-Hua],
Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
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Zheng, X., Yuan, Y., Lu, X.,
Dimensionality Reduction by Spatial-Spectral Preservation in Selected Bands,
GeoRS(55), No. 9, September 2017, pp. 5185-5197.
IEEE DOI 1709
selected band, spatial-spectral preservation, determinantal point process (DPP), BibRef

Rivera-Caicedo, J.P.[Juan Pablo], Verrelst, J.[Jochem], Muñoz-Marí, J.[Jordi], Camps-Valls, G.[Gustau], Moreno, J.[José],
Hyperspectral dimensionality reduction for biophysical variable statistical retrieval,
PandRS(132), No. 1, 2017, pp. 88-101.
Elsevier DOI 1710
Spectral, dimensionality, reduction, methods BibRef

Wu, H.[Hao], Prasad, S.[Saurabh],
Semi-supervised dimensionality reduction of hyperspectral imagery using pseudo-labels,
PR(74), No. 1, 2018, pp. 212-224.
Elsevier DOI 1711
Dimensionality reduction BibRef

Ghaffari, O.[Omid], Zoej, M.J.V.[Mohammad Javad Valadan], Mokhtarzade, M.[Mehdi],
Reducing the Effect of the Endmembers' Spectral Variability by Selecting the Optimal Spectral Bands,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
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Rocha, A.D.[Alby D.], Groen, T.A.[Thomas A.], Skidmore, A.K.[Andrew K.], Darvishzadeh, R.[Roshanak], Willemen, L.[Louise],
The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data,
PandRS(133), No. Supplement C, 2017, pp. 61-74.
Elsevier DOI 1711
Remote sensing, Model tuning, Cross-validation, Prediction accuracy, Dimensionality, Multicollinearity BibRef


Gan, X., Liu, J.,
Parallelizing band selection for hyperspectral imagery with many-threads,
ICIVC17(505-509)
IEEE DOI 1708
Acceleration, Central Processing Unit, Digital signal processing, Graphics processing units, Hyperspectral imaging, Synchronization, China accelerator, K-L divergence, band selection, many-threads BibRef

Boyarski, A.[Amit], Bronstein, A.M.[Alex M.], Bronstein, M.M.[Michael M.],
Subspace Least Squares Multidimensional Scaling,
SSVM17(681-693).
Springer DOI 1706
dimensionality reduction and visualization of high dimensional data. BibRef

Hirakawa, K.[Keigo],
Fourier Multispectral Imaging: Measuring Spectra, One Sinusoid at a Time,
CCIW17(3-12).
Springer DOI 1704
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Aghagolzadeh, M., Radha, H.,
Hyperspectral material classification under monochromatic and trichromatic sampling rates,
ICIP16(2192-2196)
IEEE DOI 1610
Cameras BibRef

Ke, T.W., Liu, T.L.,
Recursive reduction net for large-scale high-dimensional data,
ICIP16(1903-1907)
IEEE DOI 1610
Binary codes BibRef

hashjin, S.S.[S. Sharifi], Darvishi, A., Khazai, S., Hatami, F., houtki, M.J.[M. Jafari],
A Band Selection Method For Sub-pixel Target Detection In Hyperspectral Images Based On Laboratory And Field Reflectance Spectral Comparison,
ISPRS16(B7: 117-120).
DOI Link 1610
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Myasnikov, E.[Evgeny],
Nonlinear Mapping Based on Spectral Angle Preserving Principle for Hyperspectral Image Analysis,
CAIP17(II: 416-427).
Springer DOI 1708
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Earlier:
The Use of Interpolation Methods for Nonlinear Mapping,
ICCVG16(649-655).
Springer DOI 1611
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Earlier:
Evaluation of Stochastic Gradient Descent Methods for Nonlinear Mapping of Hyperspectral Data,
ICIAR16(276-283).
Springer DOI 1608
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Le Bris, A., Chehata, N., Briottet, X., Paparoditis, N.,
Extraction of Optimal Spectral Bands Using Hierarchical Band Merging Out of Hyperspectral Data,
GeoHyper15(459-465).
DOI Link 1602
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Merzouqi, M., Nhaila, H., Sarhrouni, E., Hammouch, A.,
Improved filter algorithm using inequality fano to select bands for HSI classification,
ISCV15(1-5)
IEEE DOI 1506
atmospherics BibRef

Nhaila, H., Merzouqi, M., Sarhrouni, E., Hammouch, A.,
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information,
ISCV15(1-5)
IEEE DOI 1506
data reduction BibRef

Khoder, J.[Jihan], Younes, R.[Rafic], Obeid, H.[Hussein], Khalil, M.[Mohamad],
Dimension Reduction of Hyperspectral Image with Rare Event Preserving,
IbPRIA15(621-629).
Springer DOI 1506
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Licciardi, G.A., Chanussot, J., Piscini, A.,
Spectral compression of hyperspectral images by means of nonlinear principal component analysis decorrelation,
ICIP14(5092-5096)
IEEE DOI 1502
Decorrelation BibRef

Licciardi, G.A., Chanussot, J., Vasile, G., Piscini, A.,
Enhancing hyperspectral image quality using nonlinear PCA,
ICIP14(5087-5091)
IEEE DOI 1502
Hyperspectral imaging BibRef

Bouchech, H.J.[Hamdi Jamel], Foufou, S.[Sebti], Abidi, M.[Mongi],
Multilinear Sparse Decomposition for Best Spectral Bands Selection,
ICISP14(384-391).
Springer DOI 1406
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Zhang, M.[Miao], Ding, C.[Chris],
Robust Tucker Tensor Decomposition for Effective Image Representation,
ICCV13(2448-2455)
IEEE DOI 1403
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Li, H.C.[Hai-Chang], Wang, Y.[Ying], Duan, J.Y.[Jiang-Yong], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Group sparsity based semi-supervised band selection for hyperspectral images,
ICIP13(3225-3229)
IEEE DOI 1402
Band selection;Group sparsity;Hyperspectral imaging;Smoothness prior BibRef

Bai, J.[Jun], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Classification oriented semi-supervised band selection for hyperspectral images,
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Adaptive Subspace Decomposition for Hyperspectral Data Dimensionality Reduction,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly .


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