14.2.2.4.4 Hyperspectral Data, Dimensionality Reduction

Chapter Contents (Back)
Hyperspectral. Dimensionality Reduction.
See also Hyperspectral Data Band Selection.
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
BibRef

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
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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.
Elsevier DOI 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

Yang, L.[Li],
Distance-preserving mapping of patterns to 3-space,
PRL(25), No. 1, January 2004, pp. 119-128.
Elsevier DOI 0311
Each point is mapped so that its distances to three already mapped points are preserved. BibRef

Yang, L.[Li],
Distance-preserving projection of high dimensional data,
PRL(25), No. 2, January 2004, pp. 259-266.
Elsevier DOI 0401
BibRef

Yang, L.[Li],
Distance-Preserving Projection of High-Dimensional Data for Nonlinear Dimensionality Reduction,
PAMI(26), No. 9, September 2004, pp. 1243-1246.
IEEE Abstract. 0409
BibRef
And:
Locally Multidimensional Scaling for Nonlinear Dimensionality Reduction,
ICPR06(IV: 202-205).
IEEE DOI 0609
BibRef

Yang, L.[Li],
Alignment of Overlapping Locally Scaled Patches for Multidimensional Scaling and Dimensionality Reduction,
PAMI(30), No. 3, March 2008, pp. 438-450.
IEEE DOI 0801
BibRef

Zhao, D.F.[Dong-Fang], Yang, L.[Li],
Incremental Construction of Neighborhood Graphs for Nonlinear Dimensionality Reduction,
ICPR06(III: 177-180).
IEEE DOI 0609
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

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

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

Pang, Y., Yuan, Y., Li, X.L.,
Effective Feature Extraction in High-Dimensional Space,
SMC-B(38), No. 6, December 2008, pp. 1652-1656.
IEEE DOI 0812
BibRef

Pang, Y., Yuan, Y., Li, X.L.,
Iterative Subspace Analysis Based on Feature Line Distance,
IP(18), No. 4, April 2009, pp. 903-907.
IEEE DOI 0903
BibRef

Zhang, Z.Y.[Zhen-Yue], Wang, J.[Jing], Zha, H.Y.[Hong-Yuan],
Adaptive Manifold Learning,
PAMI(34), No. 2, February 2012, pp. 253-265.
IEEE DOI 1112
Seek low-dimensional parameterization of high-dimensional data. Assume local can approximate global. BibRef

Ailon, N.[Nir], Chazelle, B.[Bernard],
Faster Dimension Reduction,
CACM(53), No. 2, February 2010, pp. 97-104.
DOI Link 1101
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). BibRef

Cardoso, Â.[Ângelo], Wichert, A.[Andreas],
Iterative random projections for high-dimensional data clustering,
PRL(33), No. 13, 1 October 2012, pp. 1749-1755.
Elsevier DOI 1208
Clustering; K-means; High-dimensional data; Random projections 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.F.[Le-Fei], 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.F.[Le-Fei], 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.F.[Le-Fei], 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.F.[Le-Fei], 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.F.[Le-Fei], 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

Zhang, L.F.[Le-Fei], Zhang, Q.[Qian], Du, B.[Bo], Huang, X.[Xin], Tang, Y.Y.[Yuan Yan], Tao, D.C.[Da-Cheng],
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images,
Cyber(48), No. 1, January 2018, pp. 16-28.
IEEE DOI 1801
Feature extraction, Hyperspectral imaging, Laplace equations, Manifolds, Sparse matrices, Feature extraction, feature selection, spectral-spatial classification BibRef

Wang, D.[Di], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Xu, Y.H.[Yong-Hao],
Adaptive Spectral-Spatial Multiscale Contextual Feature Extraction for Hyperspectral Image Classification,
GeoRS(59), No. 3, March 2021, pp. 2461-2477.
IEEE DOI 2103
Feature extraction, Iron, Support vector machines, Data mining, Hyperspectral imaging, Adaptive systems, Adaptive, multiscale contextual information BibRef

Zhang, Q.[Qian], Tian, Y.[Yuan], Yang, Y.P.[Yi-Ping], Pan, C.H.[Chun-Hong],
Automatic Spatial-Spectral Feature Selection for Hyperspectral Image via Discriminative Sparse Multimodal Learning,
GeoRS(53), No. 1, January 2015, pp. 261-279.
IEEE DOI 1410
feature selection BibRef

Zhao, R.[Rui], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Zhang, L.F.[Le-Fei],
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

Zhu, X.F.[Xiao-Feng], Huang, Z.[Zi], Yang, Y.[Yang], Shen, H.T.[Heng Tao], Xu, C.S.[Chang-Sheng], Luo, J.B.[Jie-Bo],
Self-taught dimensionality reduction on the high-dimensional small-sized data,
PR(46), No. 1, January 2013, pp. 215-229.
Elsevier DOI 1209
Dimensionality reduction; Self-taught learning; Joint sparse coding; Manifold learning; Unsupervised learning BibRef

Wu, G.[Gang], Xu, W.[Wei], Leng, H.[Huan],
Inexact and incremental bilinear Lanczos components algorithms for high dimensionality reduction and image reconstruction,
PR(48), No. 1, 2015, pp. 244-263.
Elsevier DOI 1410
Dimensionality reduction 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

Liu, R.[Raymond], Gillies, D.F.[Duncan F.],
Overfitting in linear feature extraction for classification of high-dimensional image data,
PR(53), No. 1, 2016, pp. 73-86.
Elsevier DOI 1602
Dimensionality reduction BibRef

Zhang, Y.[Yu], Wu, J.X.[Jian-Xin], Cai, J.F.[Jian-Fei],
Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval,
IP(25), No. 5, May 2016, pp. 2407-2419.
IEEE DOI 1604
BibRef
Earlier:
Compact Representation for Image Classification: To Choose or to Compress?,
CVPR14(907-914)
IEEE DOI 1409
Correlation BibRef

Taskin, G., Kaya, H., Bruzzone, L.,
Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images,
IP(26), No. 6, June 2017, pp. 2918-2928.
IEEE DOI 1705
Computational efficiency, Computational modeling, Correlation, Feature extraction, Hyperspectral imaging, Kernel, Training, Dimensionality reduction, feature selection, high dimensional model representation, hyperspectral, image, classification BibRef

Taskin, G., Crawford, M.M.,
An Out-of-Sample Extension to Manifold Learning via Meta-Modeling,
IP(28), No. 10, October 2019, pp. 5227-5237.
IEEE DOI 1909
Manifolds, Learning systems, Computational modeling, Multivariate regression, Laplace equations, 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

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

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.
Elsevier DOI 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

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

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

Ye, Z.J.[Zhi-Jing], Chen, J.Q.[Jia-Qing], Li, H.[Hong], Wei, Y.T.[Yan-Tao], Xiao, G.R.[Guang-Run], Benediktsson, J.A.[Jón Atli],
Supervised Functional Data Discriminant Analysis for Hyperspectral Image Classification,
GeoRS(58), No. 2, February 2020, pp. 841-851.
IEEE DOI 2001
Feature extraction, Splines (mathematics), Data mining, Hyperspectral imaging, Redundancy, Data models, regularized weighted fitting model (RWFM) 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

Liao, L.[Liang], Maybank, S.J.[Stephen John],
Generalized Visual Information Analysis Via Tensorial Algebra,
JMIV(62), No. 4, May 2020, pp. 560-584.
WWW Link. 2005
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
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Lunga, D., Prasad, S., Crawford, M.M., Ersoy, O.,
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feature extraction BibRef

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Elsevier DOI 1407
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data compression BibRef

Pu, H.[Hanye], Chen, Z.[Zhao], Wang, B.[Bin], Jiang, G.,
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GeoRS(52), No. 11, November 2014, pp. 7008-7022.
IEEE DOI 1407
Geologic measurements
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Ren, J., Zabalza, J., Marshall, S., Zheng, J.,
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SPMag(31), No. 4, July 2014, pp. 149-154.
IEEE DOI 1407
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Tang, Y.Y.[Yuan Yan], Yuan, H.L.[Hao-Liang], Li, L.Q.[Luo-Qing],
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GeoRS(52), No. 12, December 2014, pp. 7606-7618.
IEEE DOI 1410
geophysical image processing BibRef

Hu, S.X.[Si-Xiu], Xu, C.H.[Chun-Hua], Peng, J.T.[Jiang-Tao], Xu, Y.[Yan], Tian, L.[Long],
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geophysical image processing BibRef

Peng, J.T.[Jiang-Tao], Zhou, Y.C.[Yi-Cong], Chen, C.L.P.,
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GeoRS(53), No. 9, September 2015, pp. 4810-4824.
IEEE DOI 1506
Feature extraction BibRef

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

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Springer DOI 1811
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Hu, S.X.[Si-Xiu], Peng, J.T.[Jiang-Tao], Fu, Y.X.[Ying-Xiong], Li, L.Q.[Luo-Qing],
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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

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
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Imani, M.[Maryam], Ghassemian, H.[Hassan],
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PandRS(102), No. 1, 2015, pp. 1-13.
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Hyperspectral image BibRef

Imani, M.[Maryam], Ghassemian, H.[Hassan],
Morphology-based structure-preserving projection for spectral-spatial feature extraction and classification of hyperspectral data,
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Spatial-spectral Morphological Feature Extraction for Hyperspectral Images Classification,
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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.
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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

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.
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Feature extraction BibRef

Long, Y.[Yi], Li, H.C.[Heng-Chao], Celik, T.[Turgay], Longbotham, N.[Nathan], Emery, W.J.[William J.],
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Wang, H.R.[Hai-Rong], Celik, T.[Turgay],
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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,
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feature extraction BibRef

Bhardwaj, K.[Kaushal], Patra, S.[Swarnajyoti], Bruzzone, L.,
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GeoRS(57), No. 10, October 2019, pp. 7731-7742.
IEEE DOI 1910
geophysical image processing, hyperspectral imaging, image classification, image filtering, mathematical morphology (MM) BibRef

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],
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Feature extraction BibRef

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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,
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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],
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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,
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feature extraction 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,
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IEEE DOI 1610
Data mining BibRef

He, Z., Li, J., Liu, K., Liu, L., Tao, H.,
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IEEE DOI 1807
feature extraction, geophysical image processing, image classification, image representation, variational mode decomposition (VMD) 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

Allab, K.[Kais], Labiod, L.[Lazhar], Nadif, M.[Mohamed],
Multi-manifold matrix decomposition for data co-clustering,
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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

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, deep supervised network, hyperspectral image classification, 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, BibRef

Sun, W.W.[Wei-Wei], Yang, G.[Gang], Du, B.[Bo], Zhang, L.F.[Le-Fei], Zhang, L.P.[Liang-Pei],
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, linear, embedding, (SLRNILE) BibRef

Li, X.[Xue], Zhang, L.P.[Liang-Pei], Du, B.[Bo], Zhang, L.F.[Le-Fei],
On Gleaning Knowledge From Cross Domains by Sparse Subspace Correlation Analysis for Hyperspectral Image Classification,
GeoRS(57), No. 6, June 2019, pp. 3204-3220.
IEEE DOI 1906
Dictionaries, Learning systems, Correlation, Hyperspectral imaging, Machine learning, Canonical correlation analysis (CCA), transfer learning BibRef

Luo, F.L.[Fu-Lin], 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.
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Huang, H.[Hong], Luo, F.L.[Fu-Lin], 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

Shi, G.Y.[Guang-Yao], Huang, H.[Hong], Liu, J.[Jiamin], Li, Z.Y.[Zheng-Ying], Wang, L.H.[Li-Hua],
Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery,
RS(11), No. 20, 2019, pp. xx-yy.
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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], Chen, M.[Meili], Duan, Y.[Yule],
Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding,
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Duan, Y.[Yule], Huang, H.[Hong], Tang, Y.X.[Yu-Xiao],
Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image,
GeoRS(59), No. 1, January 2021, pp. 613-628.
IEEE DOI 2012
Manifolds, Data models, Hyperspectral imaging, Feature extraction, Dimensionality reduction (DR), discriminant sparse hypergraph, sparse representation (SR) BibRef

Shi, G.Y.[Guang-Yao], Luo, F.L.[Fu-Lin], Tang, Y.M.[Yi-Ming], Li, Y.[Yuan],
Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding,
RS(13), No. 7, 2021, pp. xx-yy.
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Huang, H.[Hong], Shi, G.Y.[Guang-Yao], He, H.B.[Hai-Bo], Duan, Y.[Yule], Luo, F.L.[Fu-Lin],
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-Spectral Manifold Learning,
Cyber(50), No. 6, June 2020, pp. 2604-2616.
IEEE DOI 2005
Manifolds, Hyperspectral imaging, Feature extraction, Dimensionality reduction, Germanium, Image reconstruction, spatial-spectral combined distance (SSCD)
See also Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image. BibRef

Huang, H.[Hong], Duan, Y.[Yule], He, H.B.[Hai-Bo], Shi, G.Y.[Guang-Yao], Luo, F.L.[Fu-Lin],
Spatial-Spectral Local Discriminant Projection for Dimensionality Reduction of Hyperspectral Image,
PandRS(156), 2019, pp. 77-93.
Elsevier DOI 1909
Hyperspectral image classification, Dimensionality reduction, Local weighted reconstruction, Spatial-spectral information
See also Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image. 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

Wu, H.[Hao], Prasad, S.[Saurabh],
Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification,
IP(27), No. 3, March 2018, pp. 1259-1270.
IEEE DOI 1801
Feature extraction, Hyperspectral imaging, Machine learning, Neural networks, Training, pseudo labels BibRef

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

Lee, G.[Geunseop],
Fast computation of the compressive hyperspectral imaging by using alternating least squares methods,
SP:IC(60), No. 1, 2018, pp. 100-106.
Elsevier DOI 1712
Hyperspectral imaging BibRef

Lee, G.[Geunseop],
An Efficient Compressive Hyperspectral Imaging Algorithm Based on Sequential Computations of Alternating Least Squares,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
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Qiao, T.[Tong], Yang, Z.J.[Zhi-Jing], Ren, J.C.[Jin-Chang], Yuen, P.[Peter], Zhao, H.M.[Hui-Min], Sun, G.[Genyun], Marshall, S.[Stephen], Benediktsson, J.A.[Jon Atli],
Joint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral imaging,
PR(77), 2018, pp. 316-328.
Elsevier DOI 1802
Hyperspectral imaging, Joint bilateral filtering, Sparse representation, Feature extraction, Data classification BibRef

Sun, H.[He], Ren, J.C.[Jin-Chang], Zhao, H.M.[Hui-Min], Yan, Y.J.[Yi-Jun], Zabalza, J.[Jaime], Marshall, S.[Stephen],
Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images,
RS(11), No. 5, 2019, pp. xx-yy.
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Fang, B.[Bei], Li, Y.[Ying], Zhang, H.K.[Hao-Kui], Chan, J.C.W.[Jonathan Cheung-Wai],
Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection,
RS(10), No. 4, 2018, pp. xx-yy.
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Fang, B.[Bei], Li, Y.[Ying], Zhang, H.K.[Hao-Kui], Chan, J.C.W.[Jonathan Cheung-Wai],
Collaborative Learning of Lightweight Convolutional Neural Network and Deep Clustering for Hyperspectral Image Semi-Supervised Classification with Limited Training Samples,
PandRS(161), 2020, pp. 164-178.
Elsevier DOI 2002
Hyperspectral image classification, Collaborative learning, Lightweight convolutional neural networks, Dual-loss, Limited training samples BibRef

Liu, Q.C.[Qi-Chao], Xiao, L.[Liang], Yang, J.X.[Jing-Xiang], Chan, J.C.W.[Jonathan Cheung-Wai],
Content-Guided Convolutional Neural Network for Hyperspectral Image Classification,
GeoRS(58), No. 9, September 2020, pp. 6124-6137.
IEEE DOI 2008
Convolution, Kernel, Shape, Feature extraction, Convolutional neural networks, Principal component analysis, latent guide map (LGM) BibRef

Fang, B.[Bei], Li, Y.[Ying], Zhang, H.K.[Hao-Kui], Chan, J.C.W.[Jonathan Cheung-Wai],
Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism,
RS(11), No. 2, 2019, pp. xx-yy.
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Ding, C.[Chen], Li, Y.[Ying], Xia, Y.[Yong], Wei, W.[Wei], Zhang, L.[Lei], Zhang, Y.N.[Yan-Ning],
Convolutional Neural Networks Based Hyperspectral Image Classification Method with Adaptive Kernels,
RS(9), No. 6, 2017, pp. xx-yy.
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Li, Y.[Ying], Zhang, H.K.[Hao-Kui], Shen, Q.A.[Qi-Ang],
Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
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Ding, C.[Chen], Li, Y.[Ying], Xia, Y.[Yong], Zhang, L.[Lei], Zhang, Y.N.[Yan-Ning],
Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
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Mei, S., Hou, J., Chen, J., Chau, L.P., Du, Q.,
Simultaneous Spatial and Spectral Low-Rank Representation of Hyperspectral Images for Classification,
GeoRS(56), No. 5, May 2018, pp. 2872-2886.
IEEE DOI 1805
Algorithm design and analysis, Atmospheric measurements, Convex functions, Hyperspectral imaging, Noise reduction, spectral variations BibRef

Torti, E.[Emanuele], Fontanella, A.[Alessandro], Plaza, A.[Antonio],
Parallel real-time virtual dimensionality estimation for hyperspectral images,
RealTimeIP(14), No. 4, April 2018, pp. 753-761.
Springer DOI 1805
BibRef

Martel, E.[Ernestina], Lazcano, R.[Raquel], López, J.[José], Madroñal, D.[Daniel], Salvador, R.[Rubén], López, S.[Sebastián], Juarez, E.[Eduardo], Guerra, R.[Raúl], Sanz, C.[César], Sarmiento, R.[Roberto],
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
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Zhang, W.Q.[Wen-Qiang], Li, X.R.[Xiao-Run], Zhao, L.Y.[Liao-Ying],
Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection,
RS(11), No. 11, 2019, pp. xx-yy.
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GeoRS(56), No. 8, August 2018, pp. 4731-4746.
IEEE DOI 1808
geophysical image processing, graph theory, hyperspectral imaging, image representation, tensors, tensor processing BibRef

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feature extraction, hyperspectral imaging, image classification, image segmentation, learning (artificial intelligence), unsupervised dimensionality reduction BibRef

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Hyperspectral imaging, Dictionaries, Kernel, Machine learning, Support vector machines, Task analysis, Block-diagonal structure, low-rank representation (LRR) BibRef

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Training, Testing, Adaptation models, Adaptive systems, Nonhomogeneous media, Sparse matrices, Hyperspectral imaging, self-paced learning (SPL) BibRef

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IEEE DOI 1903
Tensile stress, Laplace equations, Image representation, Image reconstruction, Robustness, Manifolds, Task analysis, tucker tensor decomposition (TD) BibRef

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Robust Tucker Tensor Decomposition for Effective Image Representation,
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Zhang, M., Gong, M., Mao, Y., Li, J., Wu, Y.,
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IEEE DOI 1905
convolutional neural nets, feature extraction, geophysical image processing, hyperspectral imaging, hyperspectral images (HSIs) BibRef

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Improved Estimation of the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory,
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IEEE DOI 1906
Manifolds, Training, Feature extraction, Spectral analysis, Task analysis, Training data, Geometry, semisupervised spatial-spectral regularized MLSC (S3RMLSC) BibRef

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Discrete sine cosine algorithm (SCA), feature selection, hyperspectral remote sensing image, typical surface features BibRef

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Hyperspectral remote sensing, Feature extraction, Self-adaptive optimization, Manifold margin, Discriminant features BibRef

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IEEE DOI 2011
BibRef
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Parallel and Distributed Local Fisher Discriminant Analysis to Reduce Hyperspectral Images on Cloud Computing Architectures,
ACIVS18(245-257).
Springer DOI 1810
Feature extraction, Hyperspectral imaging, Dimensionality reduction, Memory management, Hardware, local Fisher discriminant analysis (LFDA) BibRef

Zhang, M.H.[Miao-Hua], Gao, Y.S.[Yong-Sheng], Sun, C.M.[Chang-Ming], Blumenstein, M.[Michael],
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IP(30), 2021, pp. 150-162.
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Tensors, Covariance matrices, Principal component analysis, Kernel, Linear programming, Robustness, Image reconstruction, clustering BibRef

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Canonical correlation analysis, Joint dimension reduction, Clustering adaptive, High-dimensional data BibRef

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Feature extraction, Hyperspectral imaging, Data mining, Standards, Data models, Sensors, Convolutional neural network (CNN), variational autoencoder (VAE) BibRef

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Hyperspectral, Image classification, Remote sensing, Spectral-spatial attention, Wasserstein loss BibRef

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Class Specific Coders for Hyper-Spectral Image Classification,
ICIP18(3304-3308)
IEEE DOI 1809
Training, Encoding, Principal component analysis, Standards, Support vector machines, Data models, Dimensionality reduction, Auto-encoders BibRef

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IEEE DOI 2106
Manifolds, Feature extraction, Data models, Hyperspectral imaging, Periodic structures, Analytical models, Earth, subspace learning (SL) BibRef

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Alizadeh Moghaddam, S.H., Mokhtarzade, M., Alizadeh Moghaddam, S.A.,
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Hyperspectral imaging, Generators, Training, Training data, Kernel BibRef

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Geodesic Discriminant Analysis for Manifold-Valued Data,
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Manifolds, Covariance matrices, Probabilistic logic, Shape, Gaussian distribution, Dimensionality reduction, Mathematical model BibRef

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CVPV17(3001-3007)
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Earlier:
Exploiting Spatial Context in Nonlinear Mapping of Hyperspectral Image Data,
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Springer DOI 1711
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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
BibRef
Earlier:
Evaluation of Stochastic Gradient Descent Methods for Nonlinear Mapping of Hyperspectral Data,
ICIAR16(276-283).
Springer DOI 1608
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Nhaila, H., Merzouqi, M., Sarhrouni, E., Hammouch, A.,
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information,
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data reduction BibRef

Zhang, X.[Xu], Yu, F.X.[Felix X.], Guo, R.Q.[Rui-Qi], Kumar, S.[Sanjiv], Wang, S.J.[Sheng-Jin], Chang, S.F.[Shi-Fu],
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Licciardi, G.A., Chanussot, J., Vasile, G., Piscini, A.,
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ICIP14(5087-5091)
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Hyperspectral imaging BibRef

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Huang, X.M., Hsu, P.H.,
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Dimensionality reduction in hyperspectral image classification,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data Band Selection .


Last update:Aug 2, 2021 at 20:26:03