14.2.2.4.2 Spectral-Spatial Classification, Spatial-Spectral, Hyperspectral Data

Benediktsson, J.A., Kanellopoulos, I.,
Classification of Multisource and Hyperspectral Data Based on Decision Fusion,
GeoRS(37), No. 3, May 1999, pp. 1367.
IEEE Top Reference. BibRef 9905

Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.,
Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles,
GeoRS(43), No. 3, March 2005, pp. 480-491.
IEEE Abstract. 0501

See also Multisource remote sensing data classification based on consensus and pruning. BibRef

Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.,
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles,
GeoRS(46), No. 11, November 2008, pp. 3804-3814.
IEEE DOI 0812
BibRef

Ghamisi, P., Benediktsson, J.A., Sveinsson, J.R.,
Automatic Spectral-Spatial Classification Framework Based on Attribute Profiles and Supervised Feature Extraction,
GeoRS(52), No. 9, September 2014, pp. 5771-5782.
IEEE DOI 1407
data reduction BibRef

Ghamisi, P., Dalla Mura, M., Benediktsson, J.A.,
A Survey on Spectral-Spatial Classification Techniques Based on Attribute Profiles,
GeoRS(53), No. 5, May 2015, pp. 2335-2353.
IEEE DOI 1502
geophysical image processing BibRef

Ghamisi, P., Benediktsson, J.A.[Jón Atli], Ulfarsson, M.O.,
Spectral-Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields,
GeoRS(52), No. 5, May 2014, pp. 2565-2574.
IEEE DOI 1403
Hidden Markov random field (HMRF) BibRef

Yu, H.Y.[Hao-Yang], Gao, L.R.[Lian-Ru], Li, J.[Jun], Li, S.S.[Shan Shan], Zhang, B.[Bing], Benediktsson, J.A.[Jón Atli],
Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields,
RS(8), No. 4, 2016, pp. 355.
DOI Link 1604
BibRef

Tarabalka, Y.[Yuliya], Benediktsson, J.A.[Jón Atli], Chanussot, J.[Jocelyn],
Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques,
GeoRS(47), No. 8, August 2009, pp. 2973-2987.
IEEE DOI 0907
BibRef

Cui, X.M.[Xi-Min], Zheng, K.[Ke], Gao, L.R.[Lian-Ru], Zhang, B.[Bing], Yang, D.[Dong], Ren, J.C.[Jin-Chang],
Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Fu, H.[Hang], Sun, G.[Genyun], Ren, J.C.[Jin-Chang], Zhang, A.[Aizhu], Jia, X.P.[Xiu-Ping],
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Feature extraction, Principal component analysis, Covariance matrices, Support vector machines, Deep learning, multiscale 2-D-singular spectrum analysis (2-D-SSA) BibRef

Sun, G.[Genyun], Fu, H.[Hang], Ren, J.C.[Jin-Chang], Zhang, A.[Aizhu], Zabalza, J.[Jaime], Jia, X.P.[Xiu-Ping], Zhao, H.M.[Hui-Min],
SpaSSA: Superpixelwise Adaptive SSA for Unsupervised Spatial-Spectral Feature Extraction in Hyperspectral Image,
Cyber(52), No. 7, July 2022, pp. 6158-6169.
IEEE DOI 2207
Feature extraction, Data mining, Trajectory, Spectral analysis, Sun, Principal component analysis, Oceanography, Feature extraction, superpixelwise adaptive SSA (SpaSSA) BibRef

Xu, X.D.[Xiao-Dong], Li, W.[Wei], Ran, Q.[Qiong], Du, Q.[Qian], Gao, L.R.[Lian-Ru], Zhang, B.[Bing],
Multisource Remote Sensing Data Classification Based on Convolutional Neural Network,
GeoRS(56), No. 2, February 2018, pp. 937-949.
IEEE DOI 1802
Convolution, Data mining, Feature extraction, Laser radar, Neural networks, Remote sensing, Support vector machines, hyperspectral imagery (HSI) BibRef

Liu, Y.[Yao], Gao, L.R.[Lian-Ru], Xiao, C.C.[Chen-Chao], Qu, Y.[Ying], Zheng, K.[Ke], Marinoni, A.[Andrea],
Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Yu, H.Y.[Hao-Yang], Gao, L.R.[Lian-Ru], Liao, W.Z.[Wen-Zhi], Zhang, B.[Bing], Zhuang, L.[Lina], Song, M.P.[Mei-Ping], Chanussot, J.[Jocelyn],
Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification,
GeoRS(58), No. 5, May 2020, pp. 3043-3056.
IEEE DOI 2005
Hyperspectral imaging, Dictionaries, Manifolds, Testing, Correlation, Task analysis, Classification, group sparse representation (GSR), nonlocal self-similarity (NLSS) BibRef

Shi, Q.Q.[Qiao-Qiao], Li, W.[Wei], Tao, R.[Ran], Sun, X.[Xu], Gao, L.R.[Lian-Ru],
Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Yu, H.Y.[Hao-Yang], Zhang, X.[Xiao], Song, M.P.[Mei-Ping], Hu, J.C.[Jiao-Chan], Guo, Q.D.[Qian-Dong], Gao, L.R.[Lian-Ru],
Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Zhang, H.Y.[Hong-Yan], Zhai, H.[Han], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images,
GeoRS(54), No. 6, June 2016, pp. 3672-3684.
IEEE DOI 1606
geophysical image processing BibRef

Huang, S.G.[Shao-Guang], Zhang, H.Y.[Hong-Yan], Du, Q.[Qian], Pižurica, A.[Aleksandra],
Sketch-Based Subspace Clustering of Hyperspectral Images,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef
Earlier: A1, A2, A4, Only:
Joint Sparsity Based Sparse Subspace Clustering for Hyperspectral Images,
ICIP18(3878-3882)
IEEE DOI 1809
Sparse matrices, Optimization, Color, Hyperspectral imaging, Clustering methods, Hyperspectral images, joint sparsity, super-pixels segmentation BibRef

Zhai, H.[Han], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
Sparsity-Based Clustering for Large Hyperspectral Remote Sensing Images,
GeoRS(59), No. 12, December 2021, pp. 10410-10424.
IEEE DOI 2112
Dictionaries, Computational modeling, Biological system modeling, Hyperspectral imaging, Encoding, Clustering algorithms, sparse coding BibRef

Huang, S.G.[Shao-Guang], Zhang, H.Y.[Hong-Yan], Pižurica, A.[Aleksandra],
Sketched Sparse Subspace Clustering For Large-Scale Hyperspectral Images,
ICIP20(1766-1770)
IEEE DOI 2011
Sparse matrices, TV, Optimization, Dictionaries, Clustering methods, Hyperspectral imaging, Sparse subspace clustering, sketching, large-scale data BibRef

Xia, G.S.[Gui-Song], Wang, Z.F.[Zi-Feng], Xiong, C.M.[Cai-Ming], Zhang, L.P.[Liang-Pei],
Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge,
RS(7), No. 11, 2015, pp. 15014.
DOI Link 1512
BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification,
PandRS(94), No. 1, 2014, pp. 25-36.
Elsevier DOI 1407
Kernel method BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Huang, X.[Xin], Zhang, L.F.[Le-Fei],
Joint Collaborative Representation With Multitask Learning for Hyperspectral Image Classification,
GeoRS(52), No. 9, Sept 2014, pp. 5923-5936.
IEEE DOI 1407
Dictionaries BibRef

Zhai, H.[Han], Zhang, H.Y.[Hong-Yan], Xu, X.[Xiong], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Zhang, H.Y.[Hong-Yan], Zhai, H.[Han], Liao, W.Z.[Wen-Zhi], Cao, L.Q.[Li-Qin], Zhang, L.P.[Liang-Pei], Pižurica, A.[Aleksandra],
Hyperspectral Image Kernel Sparse Subspace Clustering With Spatial Max Pooling Operation,
ISPRS16(B3: 945-948).
DOI Link 1610
BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
Efficient Superpixel-Level Multitask Joint Sparse Representation for Hyperspectral Image Classification,
GeoRS(53), No. 10, October 2015, pp. 5338-5351.
IEEE DOI 1509
computational complexity BibRef

Xu, Y.H.[Yong-Hao], Du, B.[Bo], Zhang, F.[Fan], Zhang, L.P.[Liang-Pei],
Hyperspectral image classification via a random patches network,
PandRS(142), 2018, pp. 344-357.
Elsevier DOI 1807
Random Patches Network (RPNet), RandomNet, Deep learning, Feature extraction, Hyperspectral image classification BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Huang, Y., Zhang, L.P.[Liang-Pei],
Hyperspectral Image Classification by Nonlocal Joint Collaborative Representation With a Locally Adaptive Dictionary,
GeoRS(52), No. 6, June 2014, pp. 3707-3719.
IEEE DOI 1403
Collaboration BibRef

Zhang, L.P.[Liang-Pei], Huang, X., Huang, B.[Bo], Li, P.X.[Ping-Xiang],
A Pixel Shape Index Coupled With Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery,
GeoRS(44), No. 10, October 2006, pp. 2950-2961.
IEEE DOI 0609
BibRef

Zhao, J.[Ji], Zhong, Y.F.[Yan-Fei], Jia, T.Y.[Tian-Yi], Wang, X.Y.[Xin-Yu], Xu, Y.[Yao], Shu, H.[Hong], Zhang, L.P.[Liang-Pei],
Spectral-Spatial Classification of Hyperspectral Imagery with Cooperative Game,
PandRS(135), No. Supplement C, 2018, pp. 31-42.
Elsevier DOI 1712
Conditional random fields, Game theory, Hyperspectral image, Image classification, Remote sensing
See also Spectral-Spatial Unified Networks for Hyperspectral Image Classification. BibRef

Wei, L.F.[Li-Fei], Yu, M.[Ming], Zhong, Y.F.[Yan-Fei], Zhao, J.[Ji], Liang, Y.J.[Ya-Jing], Hu, X.[Xin],
Spatial-Spectral Fusion Based on Conditional Random Fields for the Fine Classification of Crops in UAV-Borne Hyperspectral Remote Sensing Imagery,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Wei, L.F.[Li-Fei], Yu, M.[Ming], Liang, Y.J.[Ya-Jing], Yuan, Z.[Ziran], Huang, C.[Can], Li, R.[Rong], Yu, Y.W.[Yi-Wei],
Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Zhong, Y.F.[Yan-Fei], Jia, T.Y.[Tian-Yi], Zhao, J.[Ji], Wang, X.Y.[Xin-Yu], Jin, S.Y.[Shu-Ying],
Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Al-khafaji, S.L., Zhou, J., Zia, A., Liew, A.W.C.,
Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images,
IP(27), No. 2, February 2018, pp. 837-850.
IEEE DOI 1712
Cameras, Feature extraction, Hyperspectral imaging, Image resolution, Transforms, spectral-spatial feature extraction BibRef

Tarabalka, Y.[Yuliya], Haavardsholm, T.V.[Trym Vegard], Kåsen, I.[Ingebjørg], Skauli, T.[Torbjørn],
Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing,
RealTimeIP(4), No. 3, August 2009, pp. xx-yy.
Springer DOI 0909
BibRef

Tu, B.[Bing], Zhang, X.F.[Xiao-Fei], Kang, X.D.[Xu-Dong], Zhang, G.Y.[Guo-Yun], Li, S.T.[Shu-Tao],
Density Peak-Based Noisy Label Detection for Hyperspectral Image Classification,
GeoRS(57), No. 3, March 2019, pp. 1573-1584.
IEEE DOI 1903
hyperspectral imaging, image classification, pattern clustering, remote sensing, support vector machines, support vector machines (SVMs) BibRef

Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao], Duan, W.[Wuhui], Ren, J.C.[Jin-Chang], Benediktsson, J.A.[Jón Atli],
Classification of Hyperspectral Images by Exploiting Spectral-Spatial Information of Superpixel via Multiple Kernels,
GeoRS(53), No. 12, December 2015, pp. 6663-6674.
IEEE DOI 1512
geophysical image processing BibRef

Fu, W., Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Benediktsson, J.A.[Jón Atli],
Contextual Online Dictionary Learning for Hyperspectral Image Classification,
GeoRS(56), No. 3, March 2018, pp. 1336-1347.
IEEE DOI 1804
hyperspectral imaging, image classification, image representation, learning (artificial intelligence), sparse representation (SR) BibRef

Tu, B.[Bing], Kuang, W., Zhao, G., Fei, H.Y.[Hong-Yan],
Hyperspectral Image Classification via Superpixel Spectral Metrics Representation,
SPLetters(25), No. 10, October 2018, pp. 1520-1524.
IEEE DOI 1810
Training, Measurement, Image segmentation, Entropy, Hyperspectral imaging, Shape, Hyperspectral image (HSI), spectral information divergence (SID) BibRef

Tu, B.[Bing], Li, N.Y.[Nan-Ying], Fang, L.Y.[Le-Yuan], Fei, H.Y.[Hong-Yan], He, D.B.[Dan-Bing],
Classification of Hyperspectral Images via Weighted Spatial Correlation Representation,
JVCIR(56), 2018, pp. 160-166.
Elsevier DOI 1811
Hyperspectral image, Superpixel, Joint sparse representation, Correlation coefficient BibRef

Yue, J.[Jun], Fang, L.Y.[Le-Yuan], He, M.[Min],
Spectral-Spatial Latent Reconstruction for Open-Set Hyperspectral Image Classification,
IP(31), No. , 2022, pp. 5227-5241.
IEEE DOI 2208
Feature extraction, Image reconstruction, Training, Hyperspectral imaging, Calibration, Convolution, open-set environment BibRef

Tu, B.[Bing], Li, N.Y.[Nan-Ying], Fang, L.Y.[Le-Yuan], Yang, X.C.[Xian-Chang], Wu, J.H.[Jian-Hui],
Hyperspectral image classification with a class-dependent spatial-spectral mixed metric,
PRL(123), 2019, pp. 16-22.
Elsevier DOI 1906
Hyperspectral image, Spectral angle mapper, Spectral information divergence, Joint sparse representation, Local mean-based nearest neighbors BibRef

Li, N.Y.[Ning-Yang], Wang, Z.H.[Zhao-Hui],
Spectral-Spatial Fused Attention Network for Hyperspectral Image Classification,
ICIP21(3832-3836)
IEEE DOI 2201
Deep learning, Correlation, Redundancy, Feature extraction, Robustness, Proposals, Hyperspectral image classification, spatial features BibRef

Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao], Kang, X.D.[Xu-Dong], Benediktsson, J.A.[Jón Atli],
Spectral-Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation,
GeoRS(52), No. 12, December 2014, pp. 7738-7749.
IEEE DOI 1410
geophysical image processing
See also Class-Specific Sparse Multiple Kernel Learning for Spectral-Spatial Hyperspectral Image Classification. BibRef

Duan, P.[Puhong], Ghamisi, P.[Pedram], Kang, X.D.[Xu-Dong], Rasti, B.[Behnood], Li, S.T.[Shu-Tao], Gloaguen, R.[Richard],
Fusion of Dual Spatial Information for Hyperspectral Image Classification,
GeoRS(59), No. 9, September 2021, pp. 7726-7738.
IEEE DOI 2109
Support vector machines, Smoothing methods, Fuses, Imaging, Feature extraction, Minerals, Task analysis, Decision fusion, structural profile (SP) BibRef

Tu, B.[Bing], Zhang, X.F.[Xiao-Fei], Kang, X.D.[Xu-Dong], Wang, J.P.[Jin-Ping], Benediktsson, J.A.[Jón Atli],
Spatial Density Peak Clustering for Hyperspectral Image Classification With Noisy Labels,
GeoRS(57), No. 7, July 2019, pp. 5085-5097.
IEEE DOI 1907
Noise measurement, Training, Correlation, Clustering algorithms, Hyperspectral imaging, Labeling, Density peak (DP) clustering, support vector machines (SVMs) BibRef

Fauvel, M., Tarabalka, Y., Benediktsson, J.A.[Jón Atli], Chanussot, J., Tilton, J.C.,
Advances in Spectral-Spatial Classification of Hyperspectral Images,
PIEEE(100), No. 3, March 2013, pp. 652-675.
IEEE DOI 1303
BibRef

Fauvel, M.[Mathieu], Chanussot, J.[Jocelyn], Benediktsson, J.A.[Jon Atli],
A spatial-spectral kernel-based approach for the classification of remote-sensing images,
PR(45), No. 1, 2012, pp. 381-392.
Elsevier DOI 1410
Hyperspectral remote-sensing images BibRef

Fang, L.Y.[Le-Yuan], He, N.J.[Nan-Jun], Li, S.T.[Shu-Tao], Ghamisi, P.[Pedram], Benediktsson, J.A.[Jón Atli],
Extinction Profiles Fusion for Hyperspectral Images Classification,
GeoRS(56), No. 3, March 2018, pp. 1803-1815.
IEEE DOI 1804
Spatial-spectral feature extraction. feature extraction, hyperspectral imaging, image classification, image fusion, EPs-F method, HSI classification, hyperspectral image (HSI) BibRef

Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao], Kang, X.D.[Xu-Dong], Benediktsson, J.A.[Jon Atli],
Spectral-Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model,
GeoRS(53), No. 8, August 2015, pp. 4186-4201.
IEEE DOI 1506
geophysical image processing BibRef

Cao, F.X.[Fa-Xian], Yang, Z.J.[Zhi-Jing], Ren, J.C.[Jin-Chang], Ling, W.K.[Wing-Kuen], Zhao, H.M.[Hui-Min], Sun, M.J.[Mei-Jun], Benediktsson, J.A.[Jón Atli],
Sparse Representation-Based Augmented Multinomial Logistic Extreme Learning Machine With Weighted Composite Features for Spectral-Spatial Classification of Hyperspectral Images,
GeoRS(56), No. 11, November 2018, pp. 6263-6279.
IEEE DOI 1811
Training, Feature extraction, Logistics, Kernel, Mathematical model, Laplace equations, Optimization, Extreme learning machine (ELM), spectral-spatial classification BibRef

Zhang, A.Z.[Ai-Zhu], Pan, Z.J.[Zhao-Jie], Fu, H.[Hang], Sun, G.Y.[Gen-Yun], Rong, J.[Jun], Ren, J.C.[Jin-Chang], Jia, X.P.[Xiu-Ping], Yao, Y.J.[Yan-Juan],
Superpixel Nonlocal Weighting Joint Sparse Representation for Hyperspectral Image Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Gao, Q.[Qishuo], Lim, S.[Samsung], Jia, X.P.[Xiu-Ping],
Improved Joint Sparse Models for Hyperspectral Image Classification Based on a Novel Neighbour Selection Strategy,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Zhang, S.Z.[Shu-Zhen], Li, S.T.[Shu-Tao], Fu, W.[Wei], Fang, L.Y.[Lei-Yuan],
Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Li, S.T.[Shu-Tao], Dian, R.[Renwei], Fang, L.Y.[Le-Yuan], Bioucas-Dias, J.M.,
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization,
IP(27), No. 8, August 2018, pp. 4118-4130.
IEEE DOI 1806
Dictionaries, Estimation, Hyperspectral imaging, Sparse matrices, Spatial resolution, Tensile stress, Super-resolution, hyperspectral imaging BibRef

Dian, R.[Renwei], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Lu, T., Bioucas-Dias, J.M.,
Nonlocal Sparse Tensor Factorization for Semiblind Hyperspectral and Multispectral Image Fusion,
Cyber(50), No. 10, October 2020, pp. 4469-4480.
IEEE DOI 2009
Tensile stress, Matrix decomposition, Dictionaries, Spatial resolution, Hyperspectral imaging, Hyperspectral imaging, sparse tensor factorization BibRef

Dian, R.[Renwei], Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao],
Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization,
CVPR17(3862-3871)
IEEE DOI 1711
BibRef
Earlier: A1, A3, A2:
Non-local sparse representation for hyperspectral image super-resolution,
ICIP16(2832-2835)
IEEE DOI 1610
Dictionaries, Encoding, Matrix decomposition, Sparse matrices, Spatial resolution, Tensile stress. Databases BibRef

Yin, H.T.[Hai-Tao], Li, S.T.[Shu-Tao], Hu, J.W.[Jian-Wen],
Single image super resolution via texture constrained sparse representation,
ICIP11(1161-1164).
IEEE DOI 1201
BibRef

Nascimento, S.M.C., Ferreira, F., and Foster, D.H.,
Statistics of spatial cone-excitation ratios in natural scenes,
JOSA-A(19), No. 8, August 2002, pp. 1484-1490.
PDF File. Dataset, Hyperspectral.
HTML Version. BibRef 0208

Foster, D.H., Nascimento, S.M.C., Amano, K.,
Information limits on neural identification of coloured surfaces in natural scenes,
Visual Neuroscience(21), 2004, pp. 331-336.
PDF File. Dataset, Hyperspectral.
HTML Version. BibRef 0400

Fauvel, M.[Mathieu], Chanussot, J.[Jocelyn], Benediktsson, J.A.[Jon Atli], Villa, A.,
Parsimonious Mahalanobis kernel for the classification of high dimensional data,
PR(46), No. 3, March 2013, pp. 845-854.
Elsevier DOI 1212
BibRef
Earlier: A1, A2, A3, Only:
Adaptive Pixel Neighborhood Definition for the Classification of Hyperspectral Images with Support Vector Machines and Composite Kernel,
ICIP08(1884-1887).
IEEE DOI 0810
SVM; High dimensional data; High dimensional discriminant analysis; Kernel methods; Hyperspectral imagery; Parsimonious Mahalanobis kernel BibRef

Fu, W., Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Benediktsson, J.A.[Jon Atli],
Adaptive Spectral-Spatial Compression of Hyperspectral Image With Sparse Representation,
GeoRS(55), No. 2, February 2017, pp. 671-682.
IEEE DOI 1702
geophysical image processing BibRef

Lu, T.[Ting], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Ma, Y.[Yi], Benediktsson, J.A.[Jon Atli],
Spectral-Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising,
GeoRS(54), No. 1, January 2016, pp. 373-385.
IEEE DOI 1601
geophysical image processing BibRef

Lu, T.[Ting], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Bruzzone, L., Benediktsson, J.A.[Jon Atli],
Set-to-Set Distance-Based Spectral-Spatial Classification of Hyperspectral Images,
GeoRS(54), No. 12, December 2016, pp. 7122-7134.
IEEE DOI 1612
geophysical image processing BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Benediktsson, J.A.[Jón Atli],
Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering,
GeoRS(52), No. 5, May 2014, pp. 2666-2677.
IEEE DOI 1403

See also Pansharpening With Matting Model. BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Benediktsson, J.A.[Jón Atli],
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering,
GeoRS(52), No. 6, June 2014, pp. 3742-3752.
IEEE DOI 1403
Accuracy BibRef

Li, J., Marpu, P.R., Plaza, A., Bioucas-Dias, J.M., Benediktsson, J.A.,
Generalized Composite Kernel Framework for Hyperspectral Image Classification,
GeoRS(51), No. 9, 2013, pp. 4816-4829.
IEEE DOI 1309
Educational institutions BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Li, M.X.[Mei-Xiu], Benediktsson, J.A.,
Extended Random Walker-Based Classification of Hyperspectral Images,
GeoRS(53), No. 1, January 2015, pp. 144-153.
IEEE DOI 1410
geophysical image processing BibRef

Sun, B., Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Benediktsson, J.A.,
Random-Walker-Based Collaborative Learning for Hyperspectral Image Classification,
GeoRS(55), No. 1, January 2017, pp. 212-222.
IEEE DOI 1701
hyperspectral imaging BibRef

Lu, T.[Ting], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Jia, X.P.[Xiu-Ping], Benediktsson, J.A.[Jón Atli],
From Subpixel to Superpixel: A Novel Fusion Framework for Hyperspectral Image Classification,
GeoRS(55), No. 8, August 2017, pp. 4398-4411.
IEEE DOI 1708
Data mining, Feature extraction, Geometry, Hyperspectral imaging, Probabilistic logic, Support vector machines, Decision fusion, feature fusion, hyperspectral image (HSI) classification, pixel, subpixel, superpixel BibRef

Li, S.T.[Shu-Tao], Lu, T.[Ting], Fang, L.Y.[Le-Yuan], Jia, X.P.[Xiu-Ping], Benediktsson, J.A.[Jón Atli],
Probabilistic Fusion of Pixel-Level and Superpixel-Level Hyperspectral Image Classification,
GeoRS(54), No. 12, December 2016, pp. 7416-7430.
IEEE DOI 1612
geophysical image processing BibRef

Jia, S.[Sen], Deng, X.L.[Xiang-Long], Zhu, J.S.[Jia-Song], Xu, M.[Meng], Zhou, J.[Jun], Jia, X.P.[Xiu-Ping],
Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification,
GeoRS(57), No. 10, October 2019, pp. 7770-7784.
IEEE DOI 1910
feature extraction, Gabor filters, geophysical image processing, hyperspectral imaging, image classification, image fusion, superpixel segmentation BibRef

Jia, S.[Sen], Deng, B.[Bin], Zhu, J.S.[Jia-Song], Jia, X.P.[Xiu-Ping], Li, Q.Q.[Qing-Quan],
Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance,
GeoRS(56), No. 2, February 2018, pp. 749-759.
IEEE DOI 1802
Feature extraction, Hyperspectral imaging, Image segmentation, Merging, Support vector machines, Training, superpixel BibRef

Jia, S.[Sen], Deng, B.[Bin], Zhu, J.S.[Jia-Song], Jia, X.P.[Xiu-Ping], Li, Q.Q.[Qing-Quan],
Superpixel-Based Multitask Learning Framework for Hyperspectral Image Classification,
GeoRS(55), No. 5, May 2017, pp. 2575-2588.
IEEE DOI 1705
filtering theory, hyperspectral imaging, remote sensing by radar, Gabor filters, classification performance, computational complexity, hyperspectral image classification, pixel-based spatial-spectral Schroedinger eigenmaps method, superpixel-based multitask learning framework, BibRef

Chen, Y.[Yushi], Huang, L.B.[Ling-Bo], Zhu, L.[Lin], Yokoya, N.[Naoto], Jia, X.P.[Xiu-Ping],
Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning,
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DOI Link 1911
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Deng, B.[Bin], Jia, S.[Sen], Shi, D.M.[Da-Ming],
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification,
GeoRS(58), No. 2, February 2020, pp. 1422-1435.
IEEE DOI 2001
Measurement, Training, Task analysis, Adaptation models, Feature extraction, Hyperspectral imaging, small sample set (3S) classification BibRef

Jia, S.[Sen], Zhao, Q.Q.[Qing-Qing], Zhuang, J.Y.[Jia-Yue], Tang, D.D.[Ding-Ding], Long, Y.Q.[Ya-Qian], Xu, M.[Meng], Zhou, J.[Jun], Li, Q.Q.[Qing-Quan],
Flexible Gabor-Based Superpixel-Level Unsupervised LDA for Hyperspectral Image Classification,
GeoRS(59), No. 12, December 2021, pp. 10394-10409.
IEEE DOI 2112
Feature extraction, Hyperspectral imaging, Training, Image segmentation, Linear discriminant analysis, Data mining, superpixel segmentation BibRef

Jia, S.[Sen], Hu, J.[Jie], Zhu, J.S.[Jia-Song], Jia, X.P.[Xiu-Ping], Li, Q.Q.[Qing-Quan],
Three-Dimensional Local Binary Patterns for Hyperspectral Imagery Classification,
GeoRS(55), No. 4, April 2017, pp. 2399-2413.
IEEE DOI 1704
feature extraction BibRef

Jia, S.[Sen], Hu, J.[Jie], Xie, Y.[Yao], Shen, L.L.[Lin-Lin], Jia, X.P.[Xiu-Ping], Li, Q.Q.[Qing-Quan],
Gabor Cube Selection Based Multitask Joint Sparse Representation for Hyperspectral Image Classification,
GeoRS(54), No. 6, June 2016, pp. 3174-3187.
IEEE DOI 1606
Gabor filters BibRef

Jia, S.[Sen], Zhuang, J.Y.[Jia-Yue], Deng, L.[Lin], Zhu, J.S.[Jia-Song], Xu, M.[Meng], Zhou, J.[Jun], Jia, X.P.[Xiu-Ping],
3-D Gaussian-Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification,
GeoRS(57), No. 11, November 2019, pp. 8813-8826.
IEEE DOI 1911
Hyperspectral imaging, Feature extraction, Principal component analysis, Training, Task analysis, Redundancy, hyperspectral imagery BibRef

Jia, S.[Sen], Deng, B.[Bin], Xie, H.M.[Hui-Min], Deng, L.[Lin],
A Gabor feature fusion framework for hyperspectral imagery classification,
ICIP17(2394-2397)
IEEE DOI 1803
Data mining, Feature extraction, Hamming distance, Hyperspectral imaging, Training, feature fusion BibRef

Jia, S.[Sen], Wu, K.L.[Kui-Lin], Zhu, J.S.[Jia-Song], Jia, X.P.[Xiu-Ping],
Spectral-Spatial Gabor Surface Feature Fusion Approach for Hyperspectral Imagery Classification,
GeoRS(57), No. 2, February 2019, pp. 1142-1154.
IEEE DOI 1901
Hyperspectral imaging, Feature extraction, Clustering algorithms, Support vector machines, Image segmentation, Data mining, hyperspectral imagery classification BibRef

Jia, S.[Sen], Shen, L.L.[Lin-Lin], Li, Q.Q.[Qing-Quan],
Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification,
GeoRS(53), No. 2, February 2015, pp. 1118-1129.
IEEE DOI 1411
compressed sensing BibRef

Shen, L.L.[Lin-Lin], Jia, S.[Sen],
Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification,
GeoRS(49), No. 12, December 2011, pp. 5039-5046.
IEEE DOI 1201
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Jia, S.[Sen], Shen, L.L.[Lin-Lin], Zhu, J., Li, Q.Q.[Qing-Quan],
A 3-D Gabor Phase-Based Coding and Matching Framework for Hyperspectral Imagery Classification,
Cyber(48), No. 4, April 2018, pp. 1176-1188.
IEEE DOI 1804
Data mining, Feature extraction, Hyperspectral imaging, Image coding, Training, Wavelet transforms, Gabor wavelets, phase coding BibRef

Jia, S.[Sen], Deng, X.L.[Xiang-Long], Xu, M.[Meng], Zhou, J.[Jun], Jia, X.P.[Xiu-Ping],
Superpixel-Level Weighted Label Propagation for Hyperspectral Image Classification,
GeoRS(58), No. 7, July 2020, pp. 5077-5091.
IEEE DOI 2006
Hyperspectral imaging, Semisupervised learning, Image segmentation, Training, Erbium, Tuning, Hyperspectral image, superpixel segmentation BibRef

Jia, S., Xie, H., Deng, X.,
Extended Morphological Profile-based Gabor Wavelets for Hyperspectral Image Classification,
ICPR18(782-787)
IEEE DOI 1812
Feature extraction, Hyperspectral imaging, Data mining, Wavelet transforms, Surface waves BibRef

Zhu, J., Hu, J., Jia, S., Jia, X., Li, Q.,
Multiple 3-D Feature Fusion Framework for Hyperspectral Image Classification,
GeoRS(56), No. 4, April 2018, pp. 1873-1886.
IEEE DOI 1804
Feature extraction, Hyperspectral imaging, Shape, Surface morphology, Wavelet transforms, Feature fusion, sparse representation BibRef

Huang, L.B.[Ling-Bo], Chen, Y.S.[Yu-Shi], He, X.[Xin], Ghamisi, P.[Pedram],
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Noise Reduction of Hyperspectral Imagery Using Hybrid Spatial-Spectral Derivative-Domain Wavelet Shrinkage,
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IEEE DOI 0602
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Liu, J., Lu, W.,
A Probabilistic Framework for Spectral-Spatial Classification of Hyperspectral Images,
GeoRS(54), No. 9, September 2016, pp. 5375-5384.
IEEE DOI 1609
estimation theory BibRef

Li, J., Bioucas-Dias, J.M., Plaza, A.[Antonio],
Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning,
GeoRS(48), No. 11, November 2010, pp. 4085-4098.
IEEE DOI 1011
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Li, J.[Jun], Bioucas-Dias, J.M., Plaza, A.[Antonio],
Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning,
GeoRS(49), No. 10, October 2011, pp. 3947-3960.
IEEE DOI 1110
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Li, J., Bioucas-Dias, J.M., Plaza, A.,
Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields,
GeoRS(50), No. 3, March 2012, pp. 809-823.
IEEE DOI 1203

See also Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing. BibRef

Li, J.[Jun], Bioucas-Dias, J.M.[José M.], Plaza, A.[Antonio],
Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning,
GeoRS(51), No. 2, February 2013, pp. 844-856.
IEEE DOI 1302
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Li, H.[Hong], Song, Y.[Yalong], Chen, C.L.P.[C.L. Philip],
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GeoRS(55), No. 9, September 2017, pp. 5302-5312.
IEEE DOI 1709
hyperspectral imaging, image classification, image fusion, HSI classification, L1-DE, MSIF, hyperspectral image classification, local 1D embedding, multiscale spatial information fusion, multiscale strategy, BibRef

Wei, Y.T.[Yan-Tao], Zhou, Y.C.[Yi-Cong], Li, H.[Hong],
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Wei, Y.T.[Yan-Tao], Zhou, Y.C.[Yi-Cong],
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IEEE DOI 1411
geophysical image processing BibRef

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

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

Hu, S.X.[Si-Xiu], Xu, C.H.[Chun-Hua], Peng, J.T.[Jiang-Tao], Xu, Y.[Yan], Tian, L.[Long],
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Springer DOI 1811
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Hu, S.X.[Si-Xiu], Peng, J.T.[Jiang-Tao], Fu, Y.X.[Ying-Xiong], Li, L.Q.[Luo-Qing],
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Chen, X.[Xiang], Chen, N.[Na], Peng, J.T.[Jiang-Tao], Sun, W.W.[Wei-Wei],
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Peng, J.T.[Jiang-Tao], Luo, T.[Tao],
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Tang, Y.Y.[Yuan Yan], Yuan, H.L.[Hao-Liang], Li, L.Q.[Luo-Qing],
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IEEE DOI 1410
geophysical image processing BibRef

Tang, Y.Y.[Yuan Yan], Lu, Y.[Yang], Yuan, H.L.[Hao-Liang],
Hyperspectral Image Classification Based on Three-Dimensional Scattering Wavelet Transform,
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IEEE DOI 1502
Gaussian processes BibRef

Luo, H.W.[Hui-Wu], Tang, Y.Y.[Yuan Yan], Wang, Y.L.[Yu-Long], Wang, J.Z.[Jian-Zhong], Yang, L.[Lina], Li, C.L.[Chun-Li], Hu, T.B.[Ting-Bo],
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feature extraction BibRef

Yuan, H., Tang, Y.Y.[Yuan Yan],
Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification,
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IEEE DOI 1704
Computational modeling BibRef

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

Zhou, Y.C.[Yi-Cong], Wei, Y.T.[Yan-Tao],
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IEEE DOI 1606
Accuracy BibRef

Xiao, G.R.[Guang-Run], Wei, Y.T.[Yan-Tao], Yao, H.[Huang], Deng, W.[Wei], Xu, J.Z.[Jia-Zhen], Pan, D.H.[Dong-Hui],
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Hyperspectral imagery classification BibRef

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IEEE DOI 1702
hyperspectral imaging BibRef

He, L.[Lin], Li, Y.Q.[Yuan-Qing], Li, X.X.[Xiao-Xin], Wu, W.[Wei],
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IEEE DOI 1502
geophysical image processing BibRef

Xu, J.H.[Jin-Huan], Liu, P.F.[Peng-Fei], Sun, L.[Le], Xiao, L.[Liang],
Discriminative Pixel-Pairwise Constraint-Guided Extreme Learning Machine for Semi-Supervised Hyperspectral Image Classification,
ICIP18(1518-1522)
IEEE DOI 1809
Hyperspectral imaging, Support vector machines, Manifolds, Laplace equations, Computational modeling, Linear programming, extreme learning machine (ELM) BibRef

Liu, J.J.[Jian-Jun], Xiao, Z.Y.[Zhi-Yong], Chen, Y.F.[Yu-Feng], Yang, J.L.[Jin-Long],
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Liu, J.J.[Jian-Jun], Wu, Z.B.[Ze-Bin], Li, J.[Jun], Plaza, A.[Antonio], Yuan, Y.H.[Yun-Hao],
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IEEE DOI 1604
Adaptation models BibRef

Hu, J.[Jie], He, Z.[Zhi], Li, J.[Jun], He, L.[Lin], Wang, Y.[Yiwen],
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He, L.[Lin], Liu, C.Y.[Chen-Ying], Li, J.[Jun], Li, Y.Q.[Yuan-Qing], Li, S.T.[Shu-Tao], Yu, Z.L.[Zhu-Liang],
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IEEE DOI 2006
Feature extraction, Harmonic analysis, Kernel, Power harmonic filters, Standards, Hyperspectral imaging, spectral-spatial-range filtering BibRef

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IEEE DOI 1407
Hyperspectral imaging BibRef

Liu, C.Y.[Chen-Ying], He, L.[Lin], Li, Z.T.[Zhe-Tao], Li, J.[Jun],
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GeoRS(56), No. 1, January 2018, pp. 341-354.
IEEE DOI 1801
Gabor filters, feature extraction, hyperspectral imaging, image classification, image filtering, overall error probability BibRef

Li, J.[Jun], Plaza, A., Bioucas-Dias, J.M.,
Integration of Hyperspectral Image Classification and Unmixing for Active Learning,
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He, L.[Lin], Li, J.[Jun], Plaza, A.[Antonio], Li, Y.Q.[Yuan-Qing],
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IEEE DOI 1703
Feature extraction BibRef

Liu, J.J.[Jian-Jun], Wu, Z.B.[Ze-Bin], Xiao, Z.Y.[Zhi-Yong], Yang, J.L.[Jin-Long],
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IEEE DOI 1302
BibRef

Sun, L.[Le], Wu, Z.B.[Ze-Bin], Liu, J.J.[Jian-Jun], Xiao, L.[Liang], Wei, Z.H.[Zhi-Hui],
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Markov processes BibRef

Liu, H.Y.[Hong-Yi], Sun, P.P.[Pei-Pei], Du, Q.[Qian], Wu, Z.B.[Ze-Bin], Wei, Z.H.[Zhi-Hui],
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IEEE DOI 1903
hyperspectral imaging, image restoration, optimisation, remote sensing, spectral-spatial prior information, total variation (TV) BibRef

Zhang, S.Q.[Shao-Quan], Li, J.[Jun], Li, H.C.[Heng-Chao], Deng, C.Z.[Cheng-Zhi], Plaza, A.[Antonio],
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IEEE DOI 1806
Complexity theory, Correlation, Hyperspectral imaging, Libraries, Sparse matrices, Hyperspectral imaging, sparse unmixing, spatially weighted unmixing BibRef

Zhang, S.Q.[Shao-Quan], Li, J.[Jun], Wu, Z.B.[Ze-Bin], Plaza, A.[Antonio],
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IEEE DOI 1810
Image edge detection, Hyperspectral imaging, Libraries, Optimization, Spatial resolution, spatial information BibRef

Deng, C.Z.[Cheng-Zhi], Chen, Y.G.[Yong-Gang], Zhang, S.Q.[Shao-Quan], Li, F.[Fan], Lai, P.F.[Peng-Fei], Su, D.[Dingli], Hu, M.[Min], Wang, S.Q.[Sheng-Qian],
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Qi, L.[Lin], Li, J.[Jie], Wang, Y.[Ying], Huang, Y.F.[Yong-Fa], Gao, X.B.[Xin-Bo],
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IEEE DOI 2012
Hyperspectral imaging, Libraries, Sparse matrices, Collaboration, Data models, Data processing, Hyperspectral imaging, spectral-spatial-weighted unmixing BibRef

Lunga, D., Ersoy, O.,
Multidimensional Artificial Field Embedding With Spatial Sensitivity,
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embedded systems spectral signature relations in hyperspectral images. BibRef

Wang, Z.Y.[Zhang-Yang], Nasrabadi, N.M., Huang, T.S.,
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Bayes methods BibRef

Wang, Z.Y.[Zhang-Yang], Nasrabadi, N.M.[Nasser M.], Huang, T.S.,
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geophysical image processing BibRef

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IEEE DOI 1506
BibRef
Earlier: ICIP14(5262-5266)
IEEE DOI 1502
hyperspectral imaging. Dictionaries BibRef

Kianisarkaleh, A.[Azadeh], Ghassemian, H.[Hassan],
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Nonparametric feature extraction BibRef

Imani, M., Ghassemian, H.,
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Fu, Y.Y.[Yuan-Yuan], Zhao, C.J.[Chun-Jiang], Wang, J.[Jihua], Jia, X.P.[Xiu-Ping], Yang, G.J.[Gui-Jun], Song, X.Y.[Xiao-Yu], Feng, H.K.[Hai-Kuan],
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IEEE DOI 1712
Feature extraction, Hyperspectral imaging, Kernel, Principal component analysis, Training, Training data, spectral-spatial BibRef

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Kumar, B.[Brajesh], Dikshit, O.[Onkar],
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IEEE DOI 1712
BibRef
Earlier:
Parallel Implementation Of Morphological Profile Based Spectral-spatial Classification Scheme For Hyperspectral Imagery,
ISPRS16(B7: 263-267).
DOI Link 1610
Correlation, FCC, Hyperspectral imaging, Labeling, Probabilistic logic, Support vector machines, Classification, support vector machine (SVM) BibRef

Kumar, B.[Brajesh],
Hyperspectral image classification using three-dimensional geometric moments,
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Fu, P.[Peng], Sun, X.[Xin], Sun, Q.S.[Quan-Sen],
Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation,
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Active learning, Multi-kernel, Domain adaptation, Hyperspectral image classification, Remote sensing BibRef

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Attribute profile, Feature selection, Genetic algorithms, Hyperspectral image, Mutual information, Remote sensing, Support vector machine BibRef

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Hyperspectral remote sensing, Spectral-spatial classification, Mutual information, Autoencoder, Support vector machine, Random forest BibRef

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IEEE DOI 1804
geophysical image processing, hyperspectral imaging, image classification, bilayer-dependency, spectral-spatial classification BibRef

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IEEE DOI 1805
Encoding, Hyperspectral imaging, Probabilistic logic, Tensile stress, Training, Uncertainty, Hybrid probabilistic, tensor sparse coding BibRef

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IEEE DOI 2302
Semantics, Image analysis, Feature extraction, Task analysis, Bridges, Convolution, Annotations, deep learning BibRef

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IEEE DOI 1908
feature extraction, hyperspectral imaging, image classification, neural nets, spatial information, spectral information, spatial-spectral information BibRef

Deng, X.Z.[Xiao-Zheng], Mao, S.[Shasha], Yang, J.[Jinyuan], Lu, S.M.[Shi-Ming], Gou, S.[Shuiping], Zhou, Y.[Youming], Jiao, L.C.[Li-Cheng],
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GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Feature extraction, Generative adversarial networks, Training, Generators, Convolution, Hyperspectral imaging, multiscale convolution BibRef

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Yan, H.P.[Huai-Ping], Wang, J.[Jun], Tang, L.[Lei], Zhang, E.[Erlei], Yan, K.[Kun], Yu, K.[Kai], Peng, J.Y.[Jin-Ye],
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Jiao, L.C.[Li-Cheng], Liang, M.M.[Miao-Miao], Chen, H.[Huan], Yang, S.Y.[Shu-Yuan], Liu, H.Y.[Hong-Ying], Cao, X.H.[Xiang-Hai],
Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification,
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IEEE DOI 1710
convolution, feature extraction, hyperspectral imaging, image fusion, neural nets, statistics, HSIC, deep fully convolutional network BibRef

Meng, Z.[Zhe], Zhao, F.[Feng], Liang, M.M.[Miao-Miao], Xie, W.[Wen],
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Zhu, M.H.[Ming-Hao], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Yang, S.Y.[Shu-Yuan], Wang, J.N.[Jia-Ning],
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GeoRS(59), No. 1, January 2021, pp. 449-462.
IEEE DOI 2012
Feature extraction, Hyperspectral imaging, Task analysis, Training, Adaptation models, Machine learning, Attention network, spectral attention BibRef

Meng, Z.[Zhe], Li, L.L.[Ling-Ling], Jiao, L.C.[Li-Cheng], Feng, Z.X.[Zhi-Xi], Tang, X.[Xu], Liang, M.M.[Miao-Miao],
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See also Fast Deep Perception Network for Remote Sensing Scene Classification, A.
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Liang, M.M.[Miao-Miao], Wang, H.[Huai], Yu, X.[Xiangchun], Meng, Z.[Zhe], Yi, J.B.[Jian-Bing], Jiao, L.C.[Li-Cheng],
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Wang, J.N.[Jia-Ning], Huang, R.H.[Run-Hu], Guo, S.Y.[Si-Ying], Li, L.H.[Lin-Hao], Zhu, M.H.[Ming-Hao], Yang, S.Y.[Shu-Yuan], Jiao, L.C.[Li-Cheng],
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GeoRS(59), No. 10, October 2021, pp. 8754-8767.
IEEE DOI 2109
Convolution, Feature extraction, Kernel, Standards, Computational modeling, Training, Neural networks, multiscale convolution BibRef

Tang, X.[Xu], Meng, F.[Fanbo], Zhang, X.R.[Xiang-Rong], Cheung, Y.M.[Yiu-Ming], Ma, J.J.[Jing-Jing], Liu, F.[Fang], Jiao, L.C.[Li-Cheng],
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GeoRS(59), No. 3, March 2021, pp. 2430-2447.
IEEE DOI 2103
Feature extraction, Task analysis, Convolution, Solid modeling, Technological innovation, Support vector machines, spatial-spectral features BibRef

Feng, J.[Jie], Wu, X.D.[Xian-De], Shang, R.H.[Rong-Hua], Sui, C.H.[Chen-Hong], Li, J.[Jie], Jiao, L.C.[Li-Cheng], Zhang, X.R.[Xiang-Rong],
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GeoRS(59), No. 6, June 2021, pp. 5054-5070.
IEEE DOI 2106
Feature extraction, Hyperspectral imaging, Machine learning, Training, Convolutional neural networks, Adaptive systems, region search BibRef

Liang, M.M.[Miao-Miao], He, Q.H.[Qing-Hua], Yu, X.C.[Xiang-Chun], Wang, H.[Huai], Meng, Z.[Zhe], Jiao, L.C.[Li-Cheng],
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Ma, W.P.[Wen-Ping], Guo, Q.Q.[Qiong-Qiong], Wu, Y.[Yue], Zhao, W.[Wei], Zhang, X.R.[Xiang-Rong], Jiao, L.C.[Li-Cheng],
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Li, L.L.[Ling-Ling], Liang, P.J.[Pu-Jiang], Ma, J.J.[Jing-Jing], Jiao, L.C.[Li-Cheng], Guo, X.H.[Xiao-Hui], Liu, F.[Fang], Sun, C.[Chen],
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Shang, R.H.[Rong-Hua], Zhang, J.Y.[Ji-Yu], Jiao, L.C.[Li-Cheng], Li, Y.Y.[Yang-Yang], Marturi, N.[Naresh], Stolkin, R.[Rustam],
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Li, A.J.[Ai-Jin], Jiao, L.C.[Li-Cheng], Zhu, H.[Hao], Li, L.L.[Ling-Ling], Liu, F.[Fang],
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GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Semantics, Feature extraction, Image segmentation, Remote sensing, Task analysis, Convolution, Spatial resolution, Boundary attention, semantic segmentation BibRef

Liu, Y., Condessa, F., Bioucas-Dias, J.M., Li, J., Du, P., Plaza, A.,
Convex Formulation for Multiband Image Classification With Superpixel-Based Spatial Regularization,
GeoRS(56), No. 5, May 2018, pp. 2704-2721.
IEEE DOI 1805
Bayes methods, Hyperspectral imaging, Image segmentation, Labeling, Optimization, Convex relaxation, graph total variation (GTV), vectorial total variation (VTV) BibRef

Fang, L., He, N., Li, S., Plaza, A.J., Plaza, J.,
A New Spatial-Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation,
GeoRS(56), No. 6, June 2018, pp. 3534-3546.
IEEE DOI 1806
Correlation, Covariance matrices, Feature extraction, Hyperspectral imaging, Iron, Principal component analysis, manifold space (MS) BibRef

He, N., Fang, L., Li, S., Plaza, A., Plaza, J.,
Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling,
GeoRS(56), No. 12, December 2018, pp. 6899-6910.
IEEE DOI 1812
Feature extraction, Remote sensing, Nonhomogeneous media, Support vector machines, Covariance matrices, remote sensing scene classification BibRef

Masiello, G.[Guido], Serio, C.[Carmine], Venafra, S.[Sara], Liuzzi, G.[Giuliano], Poutier, L.[Laurent], Göttsche, F.M.[Frank M.],
Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements,
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Acquarelli, J.[Jacopo], Marchiori, E.[Elena], Buydens, L.M.C.[Lutgarde M.C.], Tran, T.[Thanh], van Laarhoven, T.[Twan],
Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting,
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Wang, W.J.[Wen-Ju], Dou, S.G.[Shu-Guang], Jiang, Z.M.[Zhong-Min], Sun, L.J.[Liu-Jie],
A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification,
RS(10), No. 7, 2018, pp. xx-yy.
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Wang, W.J.[Wen-Ju], Dou, S.G.[Shu-Guang], Wang, S.[Sen],
Alternately Updated Spectral-Spatial Convolution Network for the Classification of Hyperspectral Images,
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Shu, L., McIsaac, K., Osinski, G.R.,
Learning Spatial-Spectral Features for Hyperspectral Image Classification,
GeoRS(56), No. 9, September 2018, pp. 5138-5147.
IEEE DOI 1809
Feature extraction, Hyperspectral imaging, Principal component analysis, Support vector machines, Kernel, spatial-spectral features BibRef

Madani, H.[Hadis], McIsaac, K.[Kenneth],
Distance Transform-Based Spectral-Spatial Feature Vector for Hyperspectral Image Classification with Stacked Autoencoder,
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Shu, L., McIsaac, K., Osinski, G.R.,
Hyperspectral Image Classification With Stacking Spectral Patches and Convolutional Neural Networks,
GeoRS(56), No. 10, October 2018, pp. 5975-5984.
IEEE DOI 1810
Hyperspectral imaging, Feature extraction, Convolution, Principal component analysis, Neural networks, Kernel, stacking spectral patches (SSP) BibRef

Liu, X.F.[Xue-Feng], Sun, Q.[Qiaoqiao], Meng, Y.[Yue], Fu, M.[Min], Bourennane, S.[Salah],
Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples,
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Appice, A.[Annalisa], Malerba, D.[Donato],
Segmentation-aided classification of hyperspectral data using spatial dependency of spectral bands,
PandRS(147), 2019, pp. 215-231.
Elsevier DOI 1901
Spectral-spatial classification, Segmentation, Local spatial dependency analysis, Curse of dimensionality BibRef

Li, Y.S.[Yan-Shan], Wang, X.C.[Xian-Chen], Huang, Q.H.[Qing-Hua], Hu, X.H.[Xiao-Hui], Xie, W.X.[Wei-Xin],
Robust multi-view representation for spatial-spectral domain in application of hyperspectral image classification,
IET-CV(13), No. 2, March 2019, pp. 90-96.
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Qing, C.M.[Chun-Mei], Ruan, J.W.[Jia-Wei], Xu, X.M.[Xiang-Min], Ren, J.C.[Jin-Chang], Zabalza, J.[Jaime],
Spatial-spectral classification of hyperspectral images: A deep learning framework with Markov Random fields based modelling,
IET-IPR(13), No. 2, February 2019, pp. 235-245.
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Li, Z.[Zhaokui], Huang, L.[Lin], He, J.R.[Jin-Rong],
A Multiscale Deep Middle-level Feature Fusion Network for Hyperspectral Classification,
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Murphy, J.M., Maggioni, M.,
Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion,
GeoRS(57), No. 3, March 2019, pp. 1829-1845.
IEEE DOI 1903
computational complexity, feature extraction, geophysical image processing, image segmentation, unsupervised learning BibRef

Polk, S.L.[Sam L.], Cui, K.N.[Kang-Ning], Chan, A.H.Y.[Aland H. Y.], Coomes, D.A.[David A.], Plemmons, R.J.[Robert J.], Murphy, J.M.[James M.],
Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images,
RS(15), No. 4, 2023, pp. xx-yy.
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Seydgar, M.[Majid], Naeini, A.A.[Amin Alizadeh], Zhang, M.M.[Meng-Meng], Li, W.[Wei], Satari, M.[Mehran],
3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images,
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Wan, Y.T.[Yu-Ting], Zhong, Y.F.[Yan-Fei], Ma, A.[Ailong],
Fully Automatic Spectral-Spatial Fuzzy Clustering Using an Adaptive Multiobjective Memetic Algorithm for Multispectral Imagery,
GeoRS(57), No. 4, April 2019, pp. 2324-2340.
IEEE DOI 1904
evolutionary computation, fuzzy set theory, image processing, Pareto optimisation, pattern clustering, remote sensing, spatial information term BibRef

Wan, Y.T.[Yu-Ting], Ma, A.[Ailong], Zhang, L.P.[Liang-Pei], Zhong, Y.F.[Yan-Fei],
Multiobjective Sine Cosine Algorithm for Remote Sensing Image Spatial-Spectral Clustering,
Cyber(52), No. 10, October 2022, pp. 11172-11186.
IEEE DOI 2209
Optimization, Remote sensing, Clustering algorithms, Linear programming, Indexes, Task analysis, Genetic algorithms, sine cosine BibRef

Liao, J.S.[Jian-Shang], Wang, L.G.[Li-Guo],
Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter,
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Mei, X.G.[Xiao-Guang], Pan, E.[Erting], Ma, Y.[Yong], Dai, X.B.[Xiao-Bing], Huang, J.[Jun], Fan, F.[Fan], Du, Q.L.[Qing-Lei], Zheng, H.[Hong], Ma, J.Y.[Jia-Yi],
Spectral-Spatial Attention Networks for Hyperspectral Image Classification,
RS(11), No. 8, 2019, pp. xx-yy.
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Leng, Q.M.[Qing-Ming], Yang, H.[Haiou], Jiang, J.J.[Jun-Jun],
Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification,
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DOI Link 1905
Label noise in training data. spectral-spatial sparse graph-based adaptive label propagation. BibRef

Gu, Y., Liu, T., Li, J.,
Superpixel Tensor Model for Spatial-Spectral Classification of Remote Sensing Images,
GeoRS(57), No. 7, July 2019, pp. 4705-4719.
IEEE DOI 1907
Feature extraction, Remote sensing, Kernel, Task analysis, Algebra, Support vector machines, Extended morphological profile (EMAP), tensor BibRef

Dong, C.H.[Chun-Hua], Naghedolfeizi, M.[Masoud], Aberra, D.[Dawit], Zeng, X.Y.[Xiang-Yan],
Spectral-Spatial Discriminant Feature Learning for Hyperspectral Image Classification,
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Cao, X.Y.[Xiang-Yong], Xu, Z.B.[Zong-Ben], Meng, D.Y.[De-Yu],
Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field,
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Mei, S., Ji, J., Geng, Y., Zhang, Z., Li, X., Du, Q.,
Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification,
GeoRS(57), No. 9, September 2019, pp. 6808-6820.
IEEE DOI 1909
Feature extraction, Hyperspectral imaging, Convolution, Task analysis, spatial-spectral BibRef

Sun, Y.J.[Yang-Jie], Fu, Z.L.[Zhong-Liang], Fan, L.[Liang],
A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
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Gao, Q., Lim, S., Jia, X.,
Spectral-Spatial Hyperspectral Image Classification Using a Multiscale Conservative Smoothing Scheme and Adaptive Sparse Representation,
GeoRS(57), No. 10, October 2019, pp. 7718-7730.
IEEE DOI 1910
geophysical image processing, hyperspectral imaging, image classification, image denoising, image representation, sparse representation classification (SRC) BibRef

Arun, P.V., Krishna Mohan, B., Porwal, A.,
Spatial-spectral feature based approach towards convolutional sparse coding of hyperspectral images,
CVIU(188), 2019, pp. 102797.
Elsevier DOI 1910
Convolutional sparse coding, Hyperspectral, Convolutional neural network BibRef

He, J.[Jiang], Li, J.[Jie], Yuan, Q.Q.[Qiang-Qiang], Li, H.F.[Hui-Fang], Shen, H.F.[Huan-Feng],
Spatial-Spectral Fusion in Different Swath Widths by a Recurrent Expanding Residual Convolutional Neural Network,
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DOI Link 1910
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Zhang, C.J.[Chun-Ju], Li, G.D.[Guan-Dong], Du, S.H.[Shi-Hong],
Multi-Scale Dense Networks for Hyperspectral Remote Sensing Image Classification,
GeoRS(57), No. 11, November 2019, pp. 9201-9222.
IEEE DOI 1911
Feature extraction, Training, Convolution, Remote sensing, Convergence, Data mining, Deep learning, spectral-spatial information BibRef

Zhong, S.W.[Sheng-Wei], Zhang, Y.[Ye], Chang, C.I.[Chein-I],
A Spectral-Spatial Feedback Close Network System for Hyperspectral Image Classification,
GeoRS(57), No. 12, December 2019, pp. 10056-10069.
IEEE DOI 1912
Support vector machines, Hyperspectral imaging, Data mining, Iterative methods, Feeds, Indexes, Edge-preserving filtering (EPF), support vector machine (SVM) BibRef

Mou, L., Zhu, X.X.,
Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification,
GeoRS(58), No. 1, January 2020, pp. 110-122.
IEEE DOI 2001
Hyperspectral imaging, Logic gates, Task analysis, Convolution, Support vector machines, Attention module, hyperspectral image classification BibRef

Mu, C.H.[Cai-Hong], Guo, Z.[Zhen], Liu, Y.[Yi],
A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
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Liu, Y.[Yi], Zhu, J.[Jian], Feng, J.J.[Jia-Jie], Mu, C.H.[Cai-Hong],
A Feature Embedding Network with Multiscale Attention for Hyperspectral Image Classification,
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Huang, H., Duan, Y., He, H., Shi, G.,
Local Linear Spatial-Spectral Probabilistic Distribution for Hyperspectral Image Classification,
GeoRS(58), No. 2, February 2020, pp. 1259-1272.
IEEE DOI 2001
Training, Feature extraction, Probabilistic logic, Support vector machines, Computational modeling, spatial-spectral reconstruction BibRef

Liu, L.Q.[Li-Qin], Shi, Z.W.[Zhen-Wei], Pan, B.[Bin], Zhang, N.[Ning], Luo, H.L.[Huan-Lin], Lan, X.C.[Xian-Chao],
Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
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Han, M.X.[Meng-Xin], Cong, R.M.[Run-Min], Li, X.Y.[Xin-Yu], Fu, H.Z.[Hua-Zhu], Lei, J.J.[Jian-Jun],
Joint Spatial-Spectral Hyperspectral Image Classification Based on Convolutional Neural Network,
PRL(130), 2020, pp. 38-45.
Elsevier DOI 2002
Hyperspectral image classification, Joint spatial-spectral, Spatial enhancement, CNN BibRef

Sun, H.[Hao], Zheng, X.T.[Xiang-Tao], Lu, X.Q.[Xiao-Qiang], Wu, S.Y.[Si-Yuan],
Spectral-Spatial Attention Network for Hyperspectral Image Classification,
GeoRS(58), No. 5, May 2020, pp. 3232-3245.
IEEE DOI 2005
Feature extraction, Hyperspectral imaging, Convolution, Training, Imaging, Sun, Attention, convolutional neural network (CNN), spectral-spatial feature extraction
See also Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network. BibRef

Zheng, X.T.[Xiang-Tao], Sun, H.[Hao], Lu, X.Q.[Xiao-Qiang], Xie, W.[Wei],
Rotation-Invariant Attention Network for Hyperspectral Image Classification,
IP(31), 2022, pp. 4251-4265.
IEEE DOI 2207
Feature extraction, Convolution, Kernel, Imaging, Hyperspectral imaging, Interference, Data mining, attention mechanism BibRef

Hong, Q.Q.[Qing-Qing], Zhong, X.[Xinyi], Chen, W.T.[Wei-Tong], Zhang, Z.H.[Zheng-Hua], Li, B.[Bin], Sun, H.[Hao], Yang, T.[Tianbao], Tan, C.[Changwei],
SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification,
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DOI Link 2212
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Mou, L.C.[Li-Chao], Lu, X.Q.[Xiao-Qiang], Li, X.L.[Xue-Long], Zhua, X.X.[Xiao Xiang],
Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification,
GeoRS(58), No. 12, December 2020, pp. 8246-8257.
IEEE DOI 2012
Hyperspectral imaging, Convolution, Task analysis, Recurrent neural networks, Semisupervised learning, semisupervised learning BibRef

Chen, L.L.[Lin-Lin], Wei, Z.H.[Zhi-Hui], Xu, Y.[Yang],
A Lightweight Spectral-Spatial Feature Extraction and Fusion Network for Hyperspectral Image Classification,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
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Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.],
Adaptable Convolutional Network for Hyperspectral Image Classification,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Plaza, J.[Javier], Plaza, A.J.[Antonio J.],
A New Deep Convolutional Neural Network for Fast Hyperspectral Image Classification,
PandRS(145), 2018, pp. 120-147.
Elsevier DOI 1810
Hyperspectral imaging, Deep learning, Convolutional neural networks (CNNs), Classification, Graphics processing units (GPUs) BibRef

Haut, J.M.[Juan M.], Paoletti, M.E.[Mercedes E.], Plaza, J.[Javier], Li, J.[Jun], Plaza, A.J.[Antonio J.],
Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach,
GeoRS(56), No. 11, November 2018, pp. 6440-6461.
IEEE DOI 1811
Hyperspectral imaging, Feature extraction, Training, Bayes methods, Imaging, Neural networks, Active learning (AL), hyperspectral remote sensing image classification BibRef

Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Plaza, J.[Javier], Plaza, A.J.[Antonio J.],
Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Fernandez-Beltran, R., Plaza, J.[Javier], Plaza, A.J.[Antonio J.], Pla, F.[Filiberto],
Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification,
GeoRS(57), No. 2, February 2019, pp. 740-754.
IEEE DOI 1901
Feature extraction, Hyperspectral imaging, Machine learning, Data models, Training, Convolutional neural networks (CNNs), residual networks (ResNets)
See also Capsule Networks for Hyperspectral Image Classification. BibRef

Haut, J.M., Gallardo, J.A., Paoletti, M.E., Cavallaro, G., Plaza, J.[Javier], Plaza, A.J.[Antonio J.], Riedel, M.,
Cloud Deep Networks for Hyperspectral Image Analysis,
GeoRS(57), No. 12, December 2019, pp. 9832-9848.
IEEE DOI 1912
Cloud computing, Neural networks, Hyperspectral imaging, Iron, Data compression, Autoencoder (AE), cloud computing, speedup BibRef

Roy, S.K.[Swalpa Kumar], Manna, S.[Suvojit], Song, T.C.[Tie-Cheng], Bruzzone, L.[Lorenzo],
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification,
GeoRS(59), No. 9, September 2021, pp. 7831-7843.
IEEE DOI 2109
Feature extraction, Kernel, Radio frequency, Hyperspectral imaging, Data mining, Neurons, Training, Channel attention, residual network (ResNet) BibRef

Roy, S.K.[Swalpa Kumar], Haut, J.M.[Juan M.], Paoletti, M.E.[Mercedes E.], Dubey, S.R.[Shiv Ram], Plaza, A.J.[Antonio J.],
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Training, Generators, Generative adversarial networks, Hyperspectral imaging, spectral-spatial hyperspectral image (HSI) classification BibRef

Hong, D., Wu, X., Ghamisi, P., Chanussot, J., Yokoya, N., Zhu, X.X.,
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification,
GeoRS(58), No. 6, June 2020, pp. 3791-3808.
IEEE DOI 2005
Attribute profile (AP), feature extraction, Fourier, frequency, hyperspectral image, invariant, remote sensing, spatial-spectral classification BibRef

Zhang, T.[Tao], Zhang, P.[Puzhao], Zhong, W.L.[Wei-Lin], Yang, Z.[Zhen], Yang, F.[Fan],
JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Chan, R.H.[Raymond H.], Kan, K.K.[Kelvin K.], Nikolova, M.[Mila], Plemmons, R.J.[Robert J.],
A two-stage method for spectral-spatial classification of hyperspectral images,
JMIV(62), No. 6-7, July 2020, pp. 790-807.
Springer DOI 2007
BibRef

Chan, R.H.[Raymond H.], Li, R.N.[Ruo-Ning],
A 3-Stage Spectral-Spatial Method for Hyperspectral Image Classification,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Sun, L.[Le], Ma, C.Y.[Chen-Yang], Chen, Y.J.[Yun-Jie], Zheng, Y.H.[Yu-Hui], Shim, H.J.[Hiuk Jae], Wu, Z.B.[Ze-Bin],
Low Rank Component Induced Spatial-Spectral Kernel Method for Hyperspectral Image Classification,
CirSysVideo(30), No. 10, October 2020, pp. 3829-3842.
IEEE DOI 2010
Feature extraction, Kernel, Support vector machines, Logistics, Data mining, Training, neighborhood identification BibRef

Sellami, A.[Akrem], Ben Abbes, A.[Ali], Barra, V.[Vincent], Farah, I.R.[Imed Riadh],
Fused 3-D spectral-spatial deep neural networks and spectral clustering for hyperspectral image classification,
PRL(138), 2020, pp. 594-600.
Elsevier DOI 2010
Hyperspectral image classification, Dimensionality reduction, Convolutional Neural Network (CNN), Band clustering, Feature extraction BibRef

Sellami, A.[Akrem], Tabbone, S.[Salvatore],
Deep neural networks-based relevant latent representation learning for hyperspectral image classification,
PR(121), 2022, pp. 108224.
Elsevier DOI 2109
Deep learning, Representation learning, Hyperspectral image classification, Feature extraction BibRef

Ren, J.S.[Jian-Si], Wang, R.X.[Ruo-Xiang], Liu, G.[Gang], Wang, Y.N.[Yuan-Ni], Wu, W.[Wei],
An SVM-Based Nested Sliding Window Approach for Spectral-Spatial Classification of Hyperspectral Images,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
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He, X.[Xin], Chen, Y.[Yushi], Lin, Z.H.[Zhou-Han],
Spatial-Spectral Transformer for Hyperspectral Image Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Mu, C.H.[Cai-Hong], Liu, Y.J.[Yi-Jin], Liu, Y.[Yi],
Hyperspectral Image Spectral-Spatial Classification Method Based on Deep Adaptive Feature Fusion,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Li, Z.W.[Zhong-Wei], Cui, X.S.[Xing-Shuai], Wang, L.Q.[Lei-Quan], Zhang, H.[Hao], Zhu, X.[Xue], Zhang, Y.J.[Ya-Jing],
Spectral and Spatial Global Context Attention for Hyperspectral Image Classification,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Zhang, Y.K.[Yao-Kang], Chen, Y.J.[Yun-Jie],
Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Cheng, K.[Kai], Wang, J.L.[Juan-Le], Yan, X.R.[Xin-Rong],
Mapping Forest Types in China with 10 m Resolution Based on Spectral-Spatial-Temporal Features,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Zhong, S.W.[Sheng-Wei], Chen, S.H.[Shu-Han], Chang, C.I.[Chein-I], Zhang, Y.[Ye],
Fusion of Spectral-Spatial Classifiers for Hyperspectral Image Classification,
GeoRS(59), No. 6, June 2021, pp. 5008-5027.
IEEE DOI 2106
Hyperspectral imaging, Fuses, Support vector machines, Data mining, Signal processing algorithms, Computer science, spectral-spatial (SS) BibRef

Yin, J.[Junru], Qi, C.S.[Chang-Sheng], Chen, Q.Q.[Qi-Qiang], Qu, J.T.[Jian-Tao],
Spatial-Spectral Network for Hyperspectral Image Classification: A 3-D CNN and Bi-LSTM Framework,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
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Lei, J.J.[Jian-Jun], Li, X.Y.[Xin-Yu], Peng, B.[Bo], Fang, L.Y.[Le-Yuan], Ling, N.[Nam], Huang, Q.M.[Qing-Ming],
Deep Spatial-Spectral Subspace Clustering for Hyperspectral Image,
CirSysVideo(31), No. 7, July 2021, pp. 2686-2697.
IEEE DOI 2107
Clustering methods, Feature extraction, Collaboration, Task analysis, Clustering algorithms, Kernel, Data mining, deep learning BibRef

Ma, K.Y.[Kenneth Yeonkong], Chang, C.I.[Chein-I],
Iterative Training Sampling Coupled With Active Learning for Semisupervised Spectral-Spatial Hyperspectral Image Classification,
GeoRS(59), No. 10, October 2021, pp. 8672-8692.
IEEE DOI 2109
Training, Feedback loop, Uncertainty, Probabilistic logic, Hyperspectral imaging, Logistics, Computer science, spectral-spatial (SS) BibRef

Lv, N.[Ning], Han, Z.[Zhen], Chen, C.[Chen], Feng, Y.J.[Yi-Jia], Su, T.[Tao], Goudos, S.[Sotirios], Wan, S.H.[Shao-Hua],
Encoding Spectral-Spatial Features for Hyperspectral Image Classification in the Satellite Internet of Things System,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, T.Y.[Tian-Yu], Shi, C.P.[Cui-Ping], Liao, D.L.[Di-Ling], Wang, L.G.[Li-Guo],
A Spectral Spatial Attention Fusion with Deformable Convolutional Residual Network for Hyperspectral Image Classification,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Shi, C.P.[Cui-Ping], Liao, D.L.[Di-Ling], Zhang, T.Y.[Tian-Yu], Wang, L.G.[Li-Guo],
Hyperspectral Image Classification Based on 3D Coordination Attention Mechanism Network,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhao, Y.F.[Yi-Fei], Yan, F.Q.[Feng-Qin],
Hyperspectral Image Classification Based on Sparse Superpixel Graph,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Meng, Z.[Zhe], Zhao, F.[Feng], Liang, M.M.[Miao-Miao],
SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
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Kavalerov, I.[Ilya], Li, W.L.[Wei-Lin], Czaja, W.[Wojciech], Chellappa, R.[Rama],
3-D Fourier Scattering Transform and Classification of Hyperspectral Images,
GeoRS(59), No. 12, December 2021, pp. 10312-10327.
IEEE DOI 2112
Scattering, Feature extraction, Artificial neural networks, Transforms, Wavelet transforms, Convolution, Hyperspectral imaging, supervised classification BibRef

Wu, H.J.[Han-Jie], Li, D.[Dan], Wang, Y.J.[Yu-Jian], Li, X.J.[Xiao-Jun], Kong, F.Q.[Fan-Qiang], Wang, Q.[Qiang],
Hyperspectral Image Classification Based on Two-Branch Spectral-Spatial-Feature Attention Network,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Farooque, G.[Ghulam], Xiao, L.[Liang], Yang, J.X.[Jing-Xiang], Sargano, A.B.[Allah Bux],
Hyperspectral Image Classification via a Novel Spectral-Spatial 3D ConvLSTM-CNN,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zhang, T.Y.[Tian-Yu], Shi, C.P.[Cui-Ping], Liao, D.L.[Di-Ling], Wang, L.G.[Li-Guo],
Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Shi, C.P.[Cui-Ping], Sun, J.W.[Jing-Wei], Wang, L.G.[Li-Guo],
Hyperspectral Image Classification Based on Spectral Multiscale Convolutional Neural Network,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Pan, H.Z.[Hai-Zhu], Liu, M.[Moqi], Ge, H.[Haimiao], Wang, L.G.[Li-Guo],
One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Ge, H.M.[Hai-Miao], Wang, L.G.[Li-Guo], Liu, M.[Moqi], Zhu, Y.X.[Yue-Xia], Zhao, X.Y.[Xiao-Yu], Pan, H.Z.[Hai-Zhu], Liu, Y.Z.[Yan-Zhong],
Two-Branch Convolutional Neural Network with Polarized Full Attention for Hyperspectral Image Classification,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Shi, C.P.[Cui-Ping], Sun, J.W.[Jing-Wei], Wang, T.Y.[Tian-Yi], Wang, L.G.[Li-Guo],
Hyperspectral Image Classification Based on a 3D Octave Convolution and 3D Multiscale Spatial Attention Network,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Pan, H.Z.[Hai-Zhu], Zhao, X.Y.[Xiao-Yu], Ge, H.M.[Hai-Miao], Liu, M.[Moqi], Shi, C.P.[Cui-Ping],
Hyperspectral Image Classification Based on Multiscale Hybrid Networks and Attention Mechanisms,
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A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Ling, J.M.[Jian-Mei], Li, L.[Lu], Wang, H.Y.[Hai-Yan],
Improved Fusion of Spatial Information into Hyperspectral Classification through the Aggregation of Constrained Segment Trees: Segment Forest,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Yuan, Y.[Yuan], Wang, C.Z.[Cheng-Ze], Jiang, Z.Y.[Zhi-Yu],
Proxy-Based Deep Learning Framework for Spectral-Spatial Hyperspectral Image Classification: Efficient and Robust,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Deep learning, Feature extraction, Robustness, Measurement, Hyperspectral imaging, Training data, Training, 3-D deep learning, proxy-based learning BibRef

Miclea, A.V.[Andreia Valentina], Terebes, R.M.[Romulus Mircea], Meza, S.[Serban], Cislariu, M.[Mihaela],
On Spectral-Spatial Classification of Hyperspectral Images Using Image Denoising and Enhancement Techniques, Wavelet Transforms and Controlled Data Set Partitioning,
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PAMI(44), No. 7, July 2022, pp. 3404-3420.
IEEE DOI 2206
BibRef
Earlier: A2, A1, A3, A4:
Hyperspectral Image Reconstruction Using Deep External and Internal Learning,
ICCV19(8558-8567)
IEEE DOI 2004
Image reconstruction, Hyperspectral imaging, Spatial resolution, Lenses, Cameras, Apertures, Testing, Compressive sensing, deep internal learning. cameras, convolutional neural nets, hyperspectral imaging, image coding, image resolution BibRef

Wang, L.Z.[Li-Zhi], Sun, C.[Chen], Fu, Y.[Ying], Kim, M.H.[Min H.], Huang, H.[Hua],
Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior,
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Liu, D.X.[Dong-Xu], Han, G.L.[Guang-Liang], Liu, P.X.[Pei-Xun], Yang, H.[Hang], Chen, D.B.[Dian-Bing], Li, Q.Q.[Qing-Qing], Wu, J.J.[Jia-Jia], Wang, Y.[Yirui],
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Liu, D.X.[Dong-Xu], Wang, Y.[Yirui], Liu, P.X.[Pei-Xun], Li, Q.Q.[Qing-Qing], Yang, H.[Hang], Chen, D.[Dianbing], Liu, Z.C.[Zhi-Chao], Han, G.L.[Guang-Liang],
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Liu, D.X.[Dong-Xu], Wang, Y.[Yirui], Liu, P.X.[Pei-Xun], Li, Q.Q.[Qing-Qing], Yang, H.[Hang], Chen, D.B.[Dian-Bing], Liu, Z.C.[Zhi-Chao], Han, G.L.[Guang-Liang],
A Multibranch Crossover Feature Attention Network for Hyperspectral Image Classification,
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Liu, D.X.[Dong-Xu], Han, G.L.[Guang-Liang], Liu, P.X.[Pei-Xun], Wang, Y.[Yirui], Yang, H.[Hang], Chen, D.B.[Dian-Bing], Li, Q.Q.[Qing-Qing], Wu, J.J.[Jia-Jia],
A Hybrid-Order Spectral-Spatial Feature Network for Hyperspectral Image Classification,
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Praveen, B.[Bishwas], Menon, V.[Vineetha],
Dual-Branch-AttentionNet: A Novel Deep-Learning-Based Spatial-Spectral Attention Methodology for Hyperspectral Data Analysis,
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Spectral-Spatial Feature Extraction With Dual Graph Autoencoder for Hyperspectral Image Clustering,
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IEEE DOI 2212
Feature extraction, Data mining, Principal component analysis, Hyperspectral imaging, Convolution, Task analysis, Decoding, graph convolution BibRef

Huang, Y.X.[Yi-Xiang], Zhang, L.[Lifu], Huang, C.P.[Chang-Ping], Qi, W.C.[Wen-Chao], Song, R.[Ruoxi],
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Huang, Y.X.[Yi-Xiang], Zhang, L.[Lifu], Qi, W.C.[Wen-Chao], Huang, C.P.[Chang-Ping], Song, R.[Ruoxi],
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Feature extraction, Task analysis, Representation learning, Image classification, efficient spectral-spatial feature learning BibRef

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Multi-Scale 3D Deep Convolutional Neural Network for Hyperspectral Image Classification,
ICIP17(3904-3908)
IEEE DOI 1803
Convolution, Feature extraction, Hyperspectral imaging, Kernel, Training, multi-scale BibRef

Hong, D., Yao, J., Wu, X., Chanussot, J., Zhu, X.,
Spatial-spectral Manifold Embedding of Hyperspectral Data,
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Dovletov, G.[Gurbandurdy], Hegemann, T.[Tobias], Pauli, J.[Josef],
Spectral-Spatial Hyperspectral Image Classification Using Cascaded Convolutional Neural Networks,
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Semisupervised classification of hyperspectral remote sensing images with spatial majority voting,
PRRS16(1-4)
IEEE DOI 1704
hyperspectral imaging BibRef

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A deep spatial/spectral descriptor of hyperspectral texture using scattering transform,
ICIP16(3568-3572)
IEEE DOI 1610
Hyperspectral imaging BibRef

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Hyperspectral image classification with support vector machines on kernel distribution embeddings,
ICIP16(1898-1902)
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Hilbert space BibRef

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A DBN-CRF for spectral-spatial classification of hyperspectral data,
ICPR16(1219-1224)
IEEE DOI 1705
Context modeling, Feature extraction, Hidden Markov models, Hyperspectral imaging, Image classification, Linear programming, Training, Conditional random field, Contextual information, Deep belief network, Deep learning, Hyperspectral, image BibRef

Menon, V.[Vineetha], Prasad, S.[Saurabh], Fowler, J.E.[James E.],
Hyperspectral classification using a composite kernel driven by nearest-neighbor spatial features,
ICIP15(2100-2104)
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composite kernel; hyperspectral classification; nearest neighbor BibRef

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Schaum, A.P., Stocker, A.,
Advanced algorithms for autonomous hyperspectral change detection,
AIPR04(33-38).
IEEE DOI 0410
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Schaum, A.P.,
Algorithms with attitude,
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IEEE DOI 1010
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Schaum, A.P.,
Advanced hyperspectral detection based on elliptically contoured distribution models and operator feedback,
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IEEE DOI 0910
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Schaum, A.P.,
Adapting to Change: The CFAR Problem in Advanced Hyperspectral Detection,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Target Detection .