Bruzzone, L.,
Serpico, S.B.,
A technique for feature selection in multiclass problems,
JRS(21), No. 3, February 2000, pp. 549.
0002
BibRef
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
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
BibRef
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
BibRef
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.
DOI Link
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
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
Ye, Z.J.[Zhi-Jing],
Qian, T.[Tao],
Zhang, L.M.[Li-Ming],
Dai, L.[Lei],
Li, H.[Hong],
Benediktsson, J.A.[Jón Atli],
Functional Feature Extraction for Hyperspectral Image Classification
With Adaptive Rational Function Approximation,
GeoRS(59), No. 9, September 2021, pp. 7680-7694.
IEEE DOI
2109
Feature extraction, Hyperspectral imaging, Data models,
Machine learning, Splines (mathematics), Function approximation,
rational orthogonal function system
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.N.[Yan-Ning],
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
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
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
BibRef
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
Lu, Q.K.[Qi-Kai],
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
BibRef
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.Y.[Han-Ye],
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
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
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
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],
Morphology-based structure-preserving projection for spectral-spatial
feature extraction and classification of hyperspectral data,
IET-IPR(13), No. 2, February 2019, pp. 270-279.
DOI Link
1902
BibRef
Dowlatshah, M.,
Ghassemian, H.,
Imani, M.,
Spatial-spectral Morphological Feature Extraction for Hyperspectral
Images Classification,
SMPR19(315-320).
DOI Link
1912
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
BibRef
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
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
BibRef
Wang, H.R.[Hai-Rong],
Celik, T.[Turgay],
Sparse representation-based hyperspectral image classification,
SIViP(12), No. 5, July 2018, pp. 1009-1017.
WWW Link.
1806
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
Bhardwaj, K.[Kaushal],
Patra, S.[Swarnajyoti],
Bruzzone, L.,
Threshold-Free Attribute Profile for Classification of Hyperspectral
Images,
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],
A Dimension Reduction Framework for HSI Classification Using Fuzzy
and Kernel NFLE Transformation,
RS(7), No. 11, 2015, pp. 14292.
DOI Link
1512
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
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
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.,
Li, J.,
Liu, K.,
Liu, L.,
Tao, H.,
Kernel Low-Rank Multitask Learning in Variational Mode Decomposition
Domain for Multi-/Hyperspectral Classification,
GeoRS(56), No. 7, July 2018, pp. 4193-4208.
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
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
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.
DOI Link
1708
BibRef
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.
DOI Link
1910
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],
Chen, M.[Meili],
Duan, Y.[Yule],
Dimensionality Reduction of Hyperspectral Image Using
Spatial-Spectral Regularized Sparse Hypergraph Embedding,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link
1905
BibRef
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
Huang, H.[Hong],
Li, Z.Y.[Zheng-Ying],
Pan, Y.S.[Yin-Song],
Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image
Classification,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Duan, Y.[Yule],
Huang, H.[Hong],
Li, Z.Y.[Zheng-Ying],
Tang, Y.X.[Yu-Xiao],
Local Manifold-Based Sparse Discriminant Learning for Feature
Extraction of Hyperspectral Image,
Cyber(51), No. 8, August 2021, pp. 4021-4034.
IEEE DOI
2108
Manifolds, Iron, Feature extraction, Optimization, Sparse matrices,
Hyperspectral imaging, Feature extraction (FE),
sparse representation (SR)
BibRef
Pan, Y.S.[Yin-Song],
Wu, J.Y.[Jun-Yuan],
Huang, H.[Hong],
Liu, J.M.[Jia-Min],
Spectral Regression Discriminant Analysis for Hyperspectral Image
Classification,
ISPRS12(XXXIX-B3:503-508).
DOI Link
1209
BibRef
Huang, H.,
Liu, J.,
Pan, Y.S.,
Semi-supervised Marginal Fisher Analysis for Hyperspectral Image
Classification,
AnnalsPRS(I-3), No. 2012, pp. 377-382.
DOI Link
1209
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.
DOI Link
2104
BibRef
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
BibRef
Qiao, T.[Tong],
Yang, Z.J.[Zhi-Jing],
Ren, J.C.[Jin-Chang],
Yuen, P.[Peter],
Zhao, H.M.[Hui-Min],
Sun, G.Y.[Gen-Yun],
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.
DOI Link
1903
BibRef
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.
DOI Link
1805
BibRef
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
Liu, Q.C.[Qi-Chao],
Xiao, L.[Liang],
Yang, J.X.[Jing-Xiang],
Wei, Z.H.[Zhi-Hui],
CNN-Enhanced Graph Convolutional Network With Pixel- and
Superpixel-Level Feature Fusion for Hyperspectral Image
Classification,
GeoRS(59), No. 10, October 2021, pp. 8657-8671.
IEEE DOI
2109
Feature extraction, Convolution, Kernel, Decoding, Training data,
Deep learning, Computational modeling,
hyperspectral image (HSI) classification
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.
DOI Link
1902
BibRef
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.
DOI Link
1706
BibRef
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
BibRef
Jiang, Y.N.[Ye-Nan],
Li, Y.[Ying],
Zou, S.R.[Shan-Rong],
Zhang, H.K.[Hao-Kui],
Bai, Y.P.[Yun-Peng],
Hyperspectral Image Classification With Spatial Consistence Using
Fully Convolutional Spatial Propagation Network,
GeoRS(59), No. 12, December 2021, pp. 10425-10437.
IEEE DOI
2112
Feature extraction, Hyperspectral imaging, Data mining, Training,
Task analysis, Correlation, Image color analysis,
hyperspectral image (HSI) classification
BibRef
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
BibRef
Mei, S.H.[Shao-Hui],
Hou, J.H.[Jun-Hui],
Chen, J.[Jie],
Chau, L.P.[Lap-Pui],
Du, Q.[Qian],
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
Yang, S.J.[Shu-Jun],
Hou, J.H.[Jun-Hui],
Jia, Y.H.[Yu-Heng],
Mei, S.H.[Shao-Hui],
Du, Q.[Qian],
Superpixel-Guided Discriminative Low-Rank Representation of
Hyperspectral Images for Classification,
IP(30), 2021, pp. 8823-8835.
IEEE DOI
2111
Image segmentation, Image restoration, Numerical models, Tensors,
Spectral analysis, Prediction algorithms, Hyperspectral imaging,
classification
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
BibRef
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.
DOI Link
1906
BibRef
An, J.L.[Jin-Liang],
Zhang, X.R.[Xiang-Rong],
Zhou, H.Y.[Hui-Yu],
Jiao, L.C.[Li-Cheng],
Tensor-Based Low-Rank Graph With Multimanifold Regularization for
Dimensionality Reduction of Hyperspectral Images,
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
An, J.L.[Jin-Liang],
Lei, J.H.[Jin-Hui],
Song, Y.Z.[Yu-Zhen],
Zhang, X.R.[Xiang-Rong],
Guo, J.[Jinmei],
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral
Images Dimensionality Reduction,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link
1907
BibRef
Jiang, J.J.[Jun-Jun],
Ma, J.Y.[Jia-Yi],
Chen, C.[Chen],
Wang, Z.Y.[Zhong-Yuan],
Cai, Z.H.[Zhi-Hua],
Wang, L.Z.[Li-Zhe],
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature
Extraction of Hyperspectral Imagery,
GeoRS(56), No. 8, August 2018, pp. 4581-4593.
IEEE DOI
1808
feature extraction, hyperspectral imaging, image classification,
image segmentation, learning (artificial intelligence),
unsupervised dimensionality reduction
BibRef
Zhang, X.[Xin],
Jiang, X.W.[Xin-Wei],
Jiang, J.J.[Jun-Jun],
Zhang, Y.S.[Yong-Shan],
Liu, X.B.[Xiao-Bo],
Cai, Z.H.[Zhi-Hua],
Spectral-Spatial and Superpixelwise PCA for Unsupervised Feature
Extraction of Hyperspectral Imagery,
GeoRS(60), 2022, pp. 1-10.
IEEE DOI
2112
Feature extraction, Principal component analysis,
Image reconstruction, Image segmentation, Data models, Erbium,
superpixel segmentation
BibRef
Gonzalez, C.[Carlos],
Lopez, S.[Sebastian],
Mozos, D.[Daniel],
Sarmiento, R.[Roberto],
A novel FPGA-based architecture for the estimation of the virtual
dimensionality in remotely sensed hyperspectral images,
RealTimeIP(15), No. 2, August 2018, pp. 297-308.
Springer DOI
1808
BibRef
Wang, Q.[Qi],
He, X.[Xiang],
Li, X.L.[Xue-Long],
Locality and Structure Regularized Low Rank Representation for
Hyperspectral Image Classification,
GeoRS(57), No. 2, February 2019, pp. 911-923.
IEEE DOI
1901
Hyperspectral imaging, Dictionaries, Kernel, Machine learning,
Support vector machines, Task analysis, Block-diagonal structure,
low-rank representation (LRR)
BibRef
Peng, J.,
Sun, W.,
Du, Q.,
Self-Paced Joint Sparse Representation for the Classification of
Hyperspectral Images,
GeoRS(57), No. 2, February 2019, pp. 1183-1194.
IEEE DOI
1901
Training, Testing, Adaptation models, Adaptive systems,
Nonhomogeneous media, Sparse matrices, Hyperspectral imaging,
self-paced learning (SPL)
BibRef
Jiang, X.W.[Xin-Wei],
Song, X.[Xin],
Zhang, Y.S.[Yong-Shan],
Jiang, J.J.[Jun-Jun],
Gao, J.B.[Jun-Bin],
Cai, Z.H.[Zhi-Hua],
Laplacian Regularized Spatial-Aware Collaborative Graph for
Discriminant Analysis of Hyperspectral Imagery,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link
1901
BibRef
Shamsolmoali, P.[Pourya],
Zareapoor, M.[Masoumeh],
Yang, J.[Jie],
Convolutional Neural Network in Network (CNNiN):
Hyperspectral Image Classification and Dimensionality Reduction,
IET-IPR(13), No. 2, February 2019, pp. 246-253.
DOI Link
1902
BibRef
Chen, W.Z.[Wei-Zhao],
Yang, Z.J.[Zhi-Jing],
Cao, F.X.[Fa-Xian],
Yan, Y.J.[Yi-Jun],
Wang, M.L.[Mei-Lin],
Qing, C.M.[Chun-Mei],
Cheng, Y.Q.[Yong-Qiang],
Dimensionality reduction based on determinantal point process and
singular spectrum analysis for hyperspectral images,
IET-IPR(13), No. 2, February 2019, pp. 299-306.
DOI Link
1902
BibRef
Jiang, B.[Bo],
Ding, C.[Chris],
Tang, J.[Jin],
Luo, B.[Bin],
Image Representation and Learning With Graph-Laplacian Tucker Tensor
Decomposition,
Cyber(49), No. 4, April 2019, pp. 1417-1426.
IEEE DOI
1903
Tensile stress, Laplace equations, Image representation,
Image reconstruction, Robustness, Manifolds, Task analysis,
tucker tensor decomposition (TD)
BibRef
Zhang, M.[Miao],
Ding, C.[Chris],
Robust Tucker Tensor Decomposition for Effective Image Representation,
ICCV13(2448-2455)
IEEE DOI
1403
BibRef
Ahmadi, S.A.[Seyyed Ali],
Mehrshad, N.[Nasser],
Razavi, S.M.[Seyyed Mohammad],
Supervised feature extraction method based on low-rank representation
with preserving local pairwise constraints for hyperspectral images,
SIViP(13), No. 3, April 2019, pp. 583-590.
Springer DOI
1904
BibRef
Zhang, M.,
Gong, M.,
Mao, Y.,
Li, J.,
Wu, Y.,
Unsupervised Feature Extraction in Hyperspectral Images Based on
Wasserstein Generative Adversarial Network,
GeoRS(57), No. 5, May 2019, pp. 2669-2688.
IEEE DOI
1905
convolutional neural nets, feature extraction,
geophysical image processing, hyperspectral imaging,
hyperspectral images (HSIs)
BibRef
Berman, M.[Mark],
Improved Estimation of the Intrinsic Dimension of a Hyperspectral
Image Using Random Matrix Theory,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link
1905
BibRef
Mohanty, R.,
Happy, S.L.,
Routray, A.,
A Semisupervised Spatial Spectral Regularized Manifold Local Scaling
Cut With HGF for Dimensionality Reduction of Hyperspectral Images,
GeoRS(57), No. 6, June 2019, pp. 3423-3435.
IEEE DOI
1906
Manifolds, Training, Feature extraction, Spectral analysis,
Task analysis, Training data, Geometry,
semisupervised spatial-spectral regularized MLSC (S3RMLSC)
BibRef
Zhang, L.[Lan],
Su, H.J.[Hong-Jun],
Shen, J.W.[Jing-Wei],
Hyperspectral Dimensionality Reduction Based on Multiscale
Superpixelwise Kernel Principal Component Analysis,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link
1906
BibRef
An, J.L.[Jin-Liang],
Song, Y.Z.[Yu-Zhen],
Guo, Y.W.[Yu-Wei],
Ma, X.X.[Xiao-Xiao],
Zhang, X.R.[Xiang-Rong],
Tensor Discriminant Analysis via Compact Feature Representation for
Hyperspectral Images Dimensionality Reduction,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link
1908
BibRef
Fernandez, D.[Daniel],
Gonzalez, C.[Carlos],
Mozos, D.[Daniel],
Lopez, S.[Sebastian],
FPGA implementation of the principal component analysis algorithm for
dimensionality reduction of hyperspectral images,
RealTimeIP(16), No. 5, October 2019, pp. 1395-1406.
Springer DOI
1911
BibRef
Inamdar, D.[Deep],
Kalacska, M.[Margaret],
Leblanc, G.[George],
Arroyo-Mora, J.P.[J. Pablo],
Characterizing and Mitigating Sensor Generated Spatial Correlations
in Airborne Hyperspectral Imaging Data,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Ren, J.[Jiansi],
Wang, R.X.[Ruo-Xiang],
Liu, G.[Gang],
Feng, R.[Ruyi],
Wang, Y.N.[Yuan-Ni],
Wu, W.[Wei],
Partitioned Relief-F Method for Dimensionality Reduction of
Hyperspectral Images,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Zhao, B.[Bin],
Ulfarsson, M.O.[Magnus O.],
Sveinsson, J.R.[Johannes R.],
Chanussot, J.[Jocelyn],
Unsupervised and Supervised Feature Extraction Methods for
Hyperspectral Images Based on Mixtures of Factor Analyzers,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Wan, Y.,
Ma, A.,
Zhong, Y.,
Hu, X.,
Zhang, L.,
Multiobjective Hyperspectral Feature Selection Based on Discrete Sine
Cosine Algorithm,
GeoRS(58), No. 5, May 2020, pp. 3601-3618.
IEEE DOI
2005
Discrete sine cosine algorithm (SCA), feature selection,
hyperspectral remote sensing image,
typical surface features
BibRef
Wang, W.N.[Wen-Ning],
Mou, X.Q.[Xuan-Qin],
Liu, X.B.[Xue-Bin],
Modified eigenvector-based feature extraction for hyperspectral image
classification using limited samples,
SIViP(14), No. 4, June 2020, pp. 711-717.
WWW Link.
2005
BibRef
Albarracín, J.F.H.[Juan F. H.],
Oliveira, R.S.[Rafael S.],
Hirota, M.[Marina],
dos Santos, J.A.[Jefersson A.],
da Silva Torres, R.[Ricardo],
A Soft Computing Approach for Selecting and Combining Spectral Bands,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link
2007
BibRef
Huang, H.[Hong],
Li, Z.Y.[Zheng-Ying],
He, H.B.[Hai-Bo],
Duan, Y.[Yule],
Yang, S.[Song],
Self-adaptive manifold discriminant analysis for feature extraction
from hyperspectral imagery,
PR(107), 2020, pp. 107487.
Elsevier DOI
2008
Hyperspectral remote sensing, Feature extraction,
Self-adaptive optimization, Manifold margin, Discriminant features
BibRef
Zaatour, R.[Rania],
Bouzidi, S.[Sonia],
Zagrouba, E.[Ezzeddine],
Unsupervised Image-Adapted Local Fisher Discriminant Analysis to
Reduce Hyperspectral Images Without Ground Truth,
GeoRS(58), No. 11, November 2020, pp. 7931-7941.
IEEE DOI
2011
BibRef
Earlier:
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],
Robust Tensor Decomposition for Image Representation Based on
Generalized Correntropy,
IP(30), 2021, pp. 150-162.
IEEE DOI
2011
Tensors, Covariance matrices, Principal component analysis, Kernel,
Linear programming, Robustness, Image reconstruction,
clustering
BibRef
Su, S.Z.[Shu-Zhi],
Fang, X.J.[Xian-Jin],
Yang, G.M.[Gao-Ming],
Ge, B.[Bin],
Zheng, P.[Ping],
Clustering adaptive canonical correlations for high-dimensional
multi-modal data,
JVCIR(71), 2020, pp. 102815.
Elsevier DOI
2009
Canonical correlation analysis, Joint dimension reduction,
Clustering adaptive, High-dimensional data
BibRef
Yu, W.,
Zhang, M.,
Shen, Y.,
Spatial Revising Variational Autoencoder-Based Feature Extraction
Method for Hyperspectral Images,
GeoRS(59), No. 2, February 2021, pp. 1410-1423.
IEEE DOI
2101
Feature extraction, Hyperspectral imaging, Data mining, Standards,
Data models, Sensors, Convolutional neural network (CNN),
variational autoencoder (VAE)
BibRef
Pande, S.[Shivam],
Banerjee, B.[Biplab],
Adaptive hybrid attention network for hyperspectral image
classification,
PRL(144), 2021, pp. 6-12.
Elsevier DOI
1806
Hyperspectral, Image classification, Remote sensing,
Spectral-spatial attention, Wasserstein loss
BibRef
Sharma, S.[Sanatan],
Goel, A.[Akashdeep],
Gune, O.[Omkar],
Banerjee, B.[Biplab],
Chaudhuri, S.[Subhasis],
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
Li, N.[Na],
Zhou, D.[Deyun],
Shi, J.[Jiao],
Zhang, M.Y.[Ming-Yang],
Wu, T.[Tao],
Gong, M.[Maoguo],
Deep Fully Convolutional Embedding Networks for Hyperspectral Images
Dimensionality Reduction,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Li, N.[Na],
Zhou, D.[Deyun],
Shi, J.[Jiao],
Wu, T.[Tao],
Gong, M.[Maoguo],
Spectral-Locational-Spatial Manifold Learning for Hyperspectral
Images Dimensionality Reduction,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Hong, D.F.[Dan-Feng],
Yokoya, N.[Naoto],
Chanussot, J.[Jocelyn],
Xu, J.[Jian],
Zhu, X.X.[Xiao Xiang],
Joint and Progressive Subspace Analysis (JPSA) With Spatial-Spectral
Manifold Alignment for Semisupervised Hyperspectral Dimensionality
Reduction,
Cyber(51), No. 7, July 2021, pp. 3602-3615.
IEEE DOI
2106
Manifolds, Feature extraction, Data models, Hyperspectral imaging,
Periodic structures, Analytical models, Earth,
subspace learning (SL)
BibRef
Xue, T.R.[Tian-Ru],
Wang, Y.M.[Yue-Ming],
Chen, Y.W.[Yu-Wei],
Jia, J.X.[Jian-Xin],
Wen, M.X.[Mao-Xing],
Guo, R.[Ran],
Wu, T.X.[Tian-Xiao],
Deng, X.[Xuan],
Mixed Noise Estimation Model for Optimized Kernel Minimum Noise
Fraction Transformation in Hyperspectral Image Dimensionality
Reduction,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Xue, T.R.[Tian-Ru],
Wang, Y.M.[Yue-Ming],
Deng, X.[Xuan],
A Novel Method for Fast Kernel Minimum Noise Fraction Transformation
in Hyperspectral Image Dimensionality Reduction,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Morales, G.[Giorgio],
Sheppard, J.W.[John W.],
Logan, R.D.[Riley D.],
Shaw, J.A.[Joseph A.],
Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy
Analysis and Greedy Spectral Selection,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Liu, H.[Hui],
Jia, Y.H.[Yu-Heng],
Hou, J.H.[Jun-Hui],
Zhang, Q.F.[Qing-Fu],
Global-Local Balanced Low-Rank Approximation of Hyperspectral Images
for Classification,
CirSysVideo(32), No. 4, April 2022, pp. 2013-2024.
IEEE DOI
2204
Tensors, Imaging, Hyperspectral imaging, Dimensionality reduction,
Optimization, Computational modeling, Hyperspectral image,
spectral variation
BibRef
Li, H.D.[Hong-Da],
Cui, J.[Jian],
Zhang, X.L.[Xin-Le],
Han, Y.Q.[Yong-Qi],
Cao, L.Y.[Li-Ying],
Dimensionality Reduction and Classification of Hyperspectral Remote
Sensing Image Feature Extraction,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Al-Alimi, D.[Dalal],
Al-Qaness, M.A.A.[Mohammed A.A.],
Cai, Z.H.[Zhi-Hua],
Alawamy, E.A.[Eman Ahmed],
IDA: Improving distribution analysis for reducing data complexity and
dimensionality in hyperspectral images,
PR(134), 2023, pp. 109096.
Elsevier DOI
2212
Feature reduction, Hyperspectral image, Classification,
Feature fusion, Feature extraction, Dimensionality reduction
BibRef
Al-Qaness, M.A.A.[Mohammed A. A.],
Wu, G.Y.[Guo-Yong],
Al-Alimi, D.[Dalal],
MGCET: MLP-mixer and Graph Convolutional Enhanced Transformer for
Hyperspectral Image Classification,
RS(16), No. 16, 2024, pp. 2892.
DOI Link
2408
BibRef
Islam, M.R.[Md Rashedul],
Siddiqa, A.[Ayasha],
Afjal, M.I.[Masud Ibn],
Uddin, M.P.[Md Palash],
Ulhaq, A.[Anwaar],
Hyperspectral Image Classification via Information Theoretic
Dimension Reduction,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Liu, Q.Y.[Qiao-Yuan],
Xue, D.L.[Dong-Lin],
Tang, Y.H.[Yan-Hui],
Zhao, Y.X.[Yong-Xian],
Ren, J.C.[Jin-Chang],
Sun, H.[Haijiang],
PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for
Unsupervised Hyperspectral Image Classification,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Fei, X.[Xuan],
Wu, S.[Sijia],
Miao, J.Y.[Jian-Yu],
Wang, G.[Guicai],
Sun, L.[Le],
Lightweight-VGG: A Fast Deep Learning Architecture Based on
Dimensionality Reduction and Nonlinear Enhancement for Hyperspectral
Image Classification,
RS(16), No. 2, 2024, pp. 259.
DOI Link
2402
BibRef
Alkhatib, M.Q.[Mohammed Q.],
Al-Saad, M.[Mina],
Aburaed, N.[Nour],
Mansoori, S.A.[Saeed Al],
Ahmad, H.A.[Hussain Al],
Dimensionality Reduction Techniques with Hydranet Framework for HSI
Classification,
ICIP22(3151-3155)
IEEE DOI
2211
Dimensionality reduction, Solid modeling, Visualization,
Convolution, Benchmark testing, HSI classification, HydraNet
BibRef
Myasnikov, E.[Evgeny],
Evaluation of Spectral Similarity Measures and Dimensionality Reduction
Techniques for Hyperspectral Images,
IMTA20(289-300).
Springer DOI
2103
BibRef
Cohen, L.,
Almog, O.,
Shoshany, M.[Maxim],
Improving Classification of Multispectral Images Based on Selected
Relations,
ISPRS20(B3:377-381).
DOI Link
2012
BibRef
Ayma, V.H.,
Ayma, V.A.,
Gutierrez, J.,
Dimensionality Reduction Via An Orthogonal Autoencoder Approach For
Hyperspectral Image Classification,
ISPRS20(B3:357-362).
DOI Link
2012
BibRef
Deepa, C.,
Shetty, A.,
Narasimhadhan, A.V.,
Quality Assessment of Dimensionality Reduction Techniques On
Hyperspectral Data: A Neural Network Based Approach,
ISPRS20(B3:389-394).
DOI Link
2012
BibRef
Alizadeh Moghaddam, S.H.,
Mokhtarzade, M.,
Alizadeh Moghaddam, S.A.,
A New Multiple Classifier System Based on a PSO Algorithm for The
Classification of Hyperspectral Images,
SMPR19(71-75).
DOI Link
1912
Particle Swarm Optimization.
BibRef
Li, J.M.[Ji-Ming],
Chen, F.J.[Fang-Jie],
Yang, D.Y.[Dong-Yong],
Generative Band Feature Enhancement for Hyperspectral Image
Classification,
ICPR18(1918-1923)
IEEE DOI
1812
Hyperspectral imaging, Generators, Training,
Training data, Kernel
BibRef
Louis, M.[Maxime],
Charlier, B.[Benjamin],
Durrleman, S.[Stanley],
Geodesic Discriminant Analysis for Manifold-Valued Data,
Diff-CVML18(445-4458)
IEEE DOI
1812
Manifolds, Covariance matrices, Probabilistic logic, Shape,
Gaussian distribution, Dimensionality reduction, Mathematical model
BibRef
Pande, S.[Shivam],
Banerjee, B.[Biplab],
Pižurica, A.[Aleksandra],
Class Reconstruction Driven Adversarial Domain Adaptation for
Hyperspectral Image Classification,
IbPRIA19(I:472-484).
Springer DOI
1910
BibRef
Tanji, K.,
Imiya, A.,
Itoh, H.,
Kuze, H.,
Manago, N.,
Linear Data Compression of Hyperspectral Images,
CVPV17(3001-3007)
IEEE DOI
1802
Arrays, Cameras, Hyperspectral imaging, Image color analysis,
Principal component analysis, Tensile stress
BibRef
Alasvand, Z.,
Naderan, M.,
Akbarizadeh, G.,
Superpixel-based feature learning for joint sparse representation of
hyperspectral images,
IPRIA17(156-159)
IEEE DOI
1712
feature extraction, graph theory, hyperspectral imaging,
image classification, image coding, image representation,
Superpixel
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
BibRef
Raytchev, B.,
Katamoto, Y.,
Koujiba, M.,
Tamaki, T.,
Kaneda, K.,
Ensemble-based local learning for high-dimensional data regression,
ICPR16(2640-2645)
IEEE DOI
1705
Bagging, Computed tomography, Data models, Histograms,
Learning systems, Linear regression, Training
BibRef
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
Myasnikov, E.[Evgeny],
Embedding Spatial Context into Spectral Angle Based Nonlinear Mapping
for Hyperspectral Image Analysis,
ICCVG18(263-274).
Springer DOI
1810
BibRef
Earlier:
Exploiting Spatial Context in Nonlinear Mapping of Hyperspectral Image
Data,
CIAP17(II:180-190).
Springer DOI
1711
BibRef
And:
Nonlinear Mapping Based on Spectral Angle Preserving Principle for
Hyperspectral Image Analysis,
CAIP17(II: 416-427).
Springer DOI
1708
BibRef
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
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
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],
Fast Orthogonal Projection Based on Kronecker Product,
ICCV15(2929-2937)
IEEE DOI
1602
Binary codes. Projetions of high dimensional data.
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
BibRef
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
Liu, L.Q.[Ling-Qiao],
Wang, L.[Lei],
A Scalable Unsupervised Feature Merging Approach to Efficient
Dimensionality Reduction of High-Dimensional Visual Data,
ICCV13(3008-3015)
IEEE DOI
1403
BibRef
Khoder, J.[Jihan],
Younes, R.[Rafic],
Ben Ouezdou, F.[Fethi],
Stability of Dimensionality Reduction Methods Applied on Artificial
Hyperspectral Images,
ICCVG12(465-474).
Springer DOI
1210
BibRef
Huang, X.M.,
Hsu, P.H.,
Comparisom Of Wavelet-Based and HHT-Based Feature Extraction Methods
for Hyperspectral Image Classification,
ISPRS12(XXXIX-B7:121-126).
DOI Link
1209
BibRef
Davidson, C.E.[Charles E.],
Ben-David, A.[Avishai],
On the use of covariance and correlation matrices in hyperspectral
detection,
AIPR11(1-6).
IEEE DOI
1204
BibRef
Ben-David, A.[Avishai],
Davidson, C.E.[Charles E.],
Estimation of hyperspectral covariance matrices,
AIPR11(1-4).
IEEE DOI
1204
BibRef
Li, W.[Wei],
Fowler, J.E.[James E.],
Decoder-side dimensionality determination for compressive-projection
principal component analysis of hyperspectral data,
ICIP11(321-324).
IEEE DOI
1201
BibRef
Datta, A.[Aloke],
Ghosh, S.[Susmita],
Ghosh, A.[Asish],
Wrapper based feature selection in hyperspectral image data using
self-adaptive differential evolution,
ICIIP11(1-6).
IEEE DOI
1112
BibRef
Chakrabarti, A.[Ayan],
Zickler, T.E.[Todd E.],
Statistics of real-world hyperspectral images,
CVPR11(193-200).
IEEE DOI
1106
BibRef
Willson, P.D.[Paul D.],
Chan, G.[Gabriel],
Yun, P.[Paul],
Vision physiology applied to hyperspectral short wave infrared imaging,
AIPR10(1-3).
IEEE DOI
1010
Means of reducing hyperspectral feature space to a multispectral feature
space that is orthogonal and optimal.
BibRef
Mehdizadeh, M.[Maryam],
MacNish, C.[Cara],
Khan, R.N.[R. Nazim],
Bennamoun, M.[Mohammed],
Semi-supervised Neighborhood Preserving Discriminant Embedding:
A Semi-supervised Subspace Learning Algorithm,
ACCV10(III: 199-212).
Springer DOI
1011
BibRef
Wen, J.H.[Jin-Huan],
Tian, Z.[Zheng],
She, H.W.[Hong-Wei],
Yan, W.D.[Wei-Dong],
Feature extraction of hyperspectral images based on preserving
neighborhood discriminant embedding,
IASP10(257-262).
IEEE DOI
1004
BibRef
Lee, C.,
Choi, E.,
Choe, J.,
Jeong, T.,
Dimension Reduction and Pre-emphasis for Compression of Hyperspectral
Images,
ICIAR04(II: 446-453).
Springer DOI
0409
BibRef
Berge, A.[Asbjørn],
Solberg, A.S.[Anne Schistad],
Improving Hyperspectral Classifiers: The Difference Between Reducing
Data Dimensionality and Reducing Classifier Parameter Complexity,
SCIA07(293-302).
Springer DOI
0706
BibRef
Marçal, A.R.S.[André R.S.],
Borges, J.S.[Janete S.],
Estimating the Natural Number of Classes on Hierarchically Clustered
Multi-spectral Images,
ICIAR05(447-455).
Springer DOI
0509
BibRef
Mordohai, P.[Philippos],
Medioni, G.,
Unsupervised dimensionality estimation and manifold learning
in high-dimensional spaces by tensor voting,
IJCAI05(798-803).
PDF File.
See also Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning.
BibRef
0500
Dixon, M.[Michael],
Jacobs, N.[Nathan],
Pless, R.[Robert],
Finding Minimal Parameterizations of Cylindrical Image Manifolds,
PercOrg06(192).
IEEE DOI
0609
High dimensional data that vary due to a few parameters.
BibRef
Zeng, H.W.[Hui-Wen],
Trussell, H.J.,
Feature Selection using a Mixed-Norm Penalty Function,
ICIP06(997-1000).
IEEE DOI
0610
BibRef
Earlier:
Dimensionality reduction in hyperspectral image classification,
ICIP04(II: 913-916).
IEEE DOI
0505
BibRef
Muhammed, H.H.,
Ammenberg, P.,
Bengtsson, E.,
Using feature-vector based analysis, based on principal component
analysis and independent component analysis, for analysing hyperspectral
images,
CIAP01(309-315).
IEEE DOI
0210
BibRef
Zhang, Y.[Ye],
Desai, M.D.[Mita D.],
Adaptive Subspace Decomposition for Hyperspectral Data Dimensionality
Reduction,
ICIP99(II:326-329).
IEEE DOI
BibRef
9900
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data Band Selection .