14.2.2.4.6 Hyperspectral Data, Dimensionality Reduction

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

Liu, D.[Dan], Chen, X.[Xi], Ma, C.[Chen], Liu, X.[Xue],
Hyperspherical Quantization: Toward Smaller and More Accurate Models,
WACV23(5251-5261)
IEEE DOI 2302
Deep learning, Quantization (signal), Costs, Computational modeling, Vector quantization, Neural networks, visual reasoning 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, Pattern recognition, 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 .

Last update:Sep 28, 2024 at 17:47:54