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
Xu, Y.Y.[Yang-Yang],
Li, X.T.[Xiang-Tai],
Yuan, H.B.[Hao-Bo],
Yang, Y.B.[Yi-Bo],
Zhang, L.F.[Le-Fei],
Multi-Task Learning with Multi-Query Transformer for Dense Prediction,
CirSysVideo(34), No. 2, February 2024, pp. 1228-1240.
IEEE DOI Code:
WWW Link.
2402
BibRef
Earlier: A1, A4, A5, Only:
Multi-Task Learning with Knowledge Distillation for Dense Prediction,
ICCV23(21493-21502)
IEEE DOI
2401
Task analysis, Transformers, Multitasking, Feature extraction,
Computational modeling, Decoding, Pipelines, Scene understanding,
transformers
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
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
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
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
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
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
BibRef
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
BibRef
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
BibRef
Bian, X.Y.[Xiao-Yong],
Chen, C.[Chen],
Xu, Y.[Yan],
Du, Q.[Qian],
Robust Hyperspectral Image Classification by Multi-Layer
Spatial-Spectral Sparse Representations,
RS(8), No. 12, 2016, pp. 985.
DOI Link
1612
BibRef
Pan, L.[Lei],
Li, H.C.[Heng-Chao],
Ni, J.[Jun],
Chen, C.[Chen],
Chen, X.D.[Xiang-Dong],
Du, Q.[Qian],
GPU-based fast hyperspectral image classification using joint sparse
representation with spectral consistency constraint,
RealTimeIP(14), No. 3, October 2018, pp. 463-475.
Springer DOI
1811
BibRef
Peng, J.T.[Jiang-Tao],
Zhou, Y.C.[Yi-Cong],
Chen, C.L.P.,
Region-Kernel-Based Support Vector Machines for Hyperspectral Image
Classification,
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],
Peng, J.T.[Jiang-Tao],
Fu, Y.X.[Ying-Xiong],
Li, L.Q.[Luo-Qing],
Kernel Joint Sparse Representation Based on Self-Paced Learning for
Hyperspectral Image Classification,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link
1905
BibRef
Hu, S.X.[Si-Xiu],
Xu, C.H.[Chun-Hua],
Peng, J.T.[Jiang-Tao],
Xu, Y.[Yan],
Tian, L.[Long],
Weighted Kernel joint sparse representation for hyperspectral image
classification,
IET-IPR(13), No. 2, February 2019, pp. 254-260.
DOI Link
1902
BibRef
Chen, X.[Xiang],
Chen, N.[Na],
Peng, J.T.[Jiang-Tao],
Sun, W.W.[Wei-Wei],
Local Matrix Feature-Based Kernel Joint Sparse Representation for
Hyperspectral Image Classification,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Peng, J.T.[Jiang-Tao],
Luo, T.[Tao],
Sparse matrix transform-based linear discriminant analysis for
hyperspectral image classification,
SIViP(10), No. 4, April 2016, pp. 761-768.
WWW Link.
1604
BibRef
Tang, Y.Y.[Yuan Yan],
Yuan, H.L.[Hao-Liang],
Li, L.Q.[Luo-Qing],
Manifold-Based Sparse Representation for Hyperspectral Image
Classification,
GeoRS(52), No. 12, December 2014, pp. 7606-7618.
IEEE DOI
1410
geophysical image processing
BibRef
Tang, Y.Y.[Yuan Yan],
Lu, Y.[Yang],
Yuan, H.L.[Hao-Liang],
Hyperspectral Image Classification Based on Three-Dimensional
Scattering Wavelet Transform,
GeoRS(53), No. 5, May 2015, pp. 2467-2480.
IEEE DOI
1502
Gaussian processes
BibRef
Luo, H.W.[Hui-Wu],
Tang, Y.Y.[Yuan Yan],
Wang, Y.L.[Yu-Long],
Wang, J.Z.[Jian-Zhong],
Yang, L.[Lina],
Li, C.L.[Chun-Li],
Hu, T.B.[Ting-Bo],
Hyperspectral Image Classification Based on Spectral-Spatial
One-Dimensional Manifold Embedding,
GeoRS(54), No. 9, September 2016, pp. 5319-5340.
IEEE DOI
1609
feature extraction
BibRef
Yuan, H.,
Tang, Y.Y.[Yuan Yan],
Spectral-Spatial Shared Linear Regression for Hyperspectral Image
Classification,
Cyber(47), No. 4, April 2017, pp. 934-945.
IEEE DOI
1704
Computational modeling
BibRef
Luo, H.W.[Hui-Wu],
Wang, Y.L.[Yu-Long],
Tang, Y.Y.[Yuan Yan],
Li, C.L.[Chun-Li],
Wang, J.Z.[Jian-Zhong],
Hyperspectral image classification using distance metric based
1-dimensional manifold embedding,
ICWAPR16(247-251)
IEEE DOI
1611
Hyperspectral imaging
BibRef
Zhou, Y.C.[Yi-Cong],
Wei, Y.T.[Yan-Tao],
Learning Hierarchical Spectral-Spatial Features for Hyperspectral
Image Classification,
Cyber(46), No. 7, July 2016, pp. 1667-1678.
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],
Hierarchical broad learning system for hyperspectral image
classification,
IET-IPR(16), No. 2, 2022, pp. 554-566.
DOI Link
2201
BibRef
Wang, Y.[Yi],
Song, H.W.[Hai-Wei],
Zhang, Y.[Yan],
Spectral-Spatial Classification of Hyperspectral Images Using Joint
Bilateral Filter and Graph Cut Based Model,
RS(8), No. 9, 2016, pp. 748.
DOI Link
1610
BibRef
Appice, A.[Annalisa],
Guccione, P.[Pietro],
Malerba, D.[Donato],
A novel spectral-spatial co-training algorithm for the transductive
classification of hyperspectral imagery data,
PR(63), No. 1, 2017, pp. 229-245.
Elsevier DOI
1612
Hyperspectral imagery classification
BibRef
Liang, J.[Jie],
Zhou, J.[Jun],
Qian, Y.T.[Yun-Tao],
Wen, L.[Lian],
Bai, X.[Xiao],
Gao, Y.S.[Yong-Sheng],
On the Sampling Strategy for Evaluation of Spectral-Spatial Methods
in Hyperspectral Image Classification,
GeoRS(55), No. 2, February 2017, pp. 862-880.
IEEE DOI
1702
hyperspectral imaging
BibRef
Lunga, D.,
Ersoy, O.,
Multidimensional Artificial Field Embedding With Spatial Sensitivity,
GeoRS(52), No. 2, February 2014, pp. 1518-1532.
IEEE DOI
1402
embedded systems
spectral signature relations in hyperspectral images.
BibRef
Wang, Z.Y.[Zhang-Yang],
Nasrabadi, N.M.,
Huang, T.S.,
Spatial-Spectral Classification of Hyperspectral Images Using
Discriminative Dictionary Designed by Learning Vector Quantization,
GeoRS(52), No. 8, August 2014, pp. 4808-4822.
IEEE DOI
1403
Bayes methods
BibRef
Wang, Z.Y.[Zhang-Yang],
Nasrabadi, N.M.[Nasser M.],
Huang, T.S.,
Semisupervised Hyperspectral Classification Using Task-Driven
Dictionary Learning With Laplacian Regularization,
GeoRS(53), No. 3, March 2015, pp. 1161-1173.
IEEE DOI
1412
geophysical image processing
BibRef
Sun, X.X.[Xiao-Xia],
Nasrabadi, N.M.[Nasser M.],
Tran, T.D.[Trac D.],
Task-Driven Dictionary Learning for Hyperspectral Image
Classification With Structured Sparsity Constraints,
GeoRS(53), No. 8, August 2015, pp. 4457-4471.
IEEE DOI
1506
BibRef
Earlier:
ICIP14(5262-5266)
IEEE DOI
1502
hyperspectral imaging.
Dictionaries
BibRef
Kianisarkaleh, A.[Azadeh],
Ghassemian, H.[Hassan],
Nonparametric feature extraction for classification of hyperspectral
images with limited training samples,
PandRS(119), No. 1, 2016, pp. 64-78.
Elsevier DOI
1610
Nonparametric feature extraction
BibRef
Imani, M.,
Ghassemian, H.,
Boundary Based Supervised Classification of Hyperspectral Images with
Limited Training Samples,
SMPR13(203-207).
DOI Link
1311
BibRef
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],
An Improved Combination of Spectral and Spatial Features for
Vegetation Classification in Hyperspectral Images,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link
1704
BibRef
Sui, C.,
Tian, Y.,
Xu, Y.,
Xie, Y.,
Weighted Spectral-Spatial Classification of Hyperspectral Images via
Class-Specific Band Contribution,
GeoRS(55), No. 12, December 2017, pp. 7003-7017.
IEEE DOI
1712
Feature extraction, Hyperspectral imaging, Kernel,
Principal component analysis, Training, Training data,
spectral-spatial
BibRef
Ahmad, M.[Muhammad],
Khan, A.M.[Adil Mehmood],
Hussain, R.[Rasheed],
Graph-based spatial-spectral feature learning for hyperspectral image
classification,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1310-1316.
DOI Link
1712
BibRef
Kumar, B.[Brajesh],
Dikshit, O.[Onkar],
Spectral Contextual Classification of Hyperspectral Imagery With
Probabilistic Relaxation Labeling,
Cyber(47), No. 12, December 2017, pp. 4380-4391.
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,
IET-IPR(14), No. 10, August 2020, pp. 2175-2186.
DOI Link
2008
BibRef
Fu, P.[Peng],
Sun, X.[Xin],
Sun, Q.S.[Quan-Sen],
Hyperspectral Image Segmentation via Frequency-Based Similarity for
Mixed Noise Estimation,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link
1802
BibRef
Cao, F.[Faxian],
Yang, Z.J.[Zhi-Jing],
Ren, J.C.[Jin-Chang],
Ling, W.K.[Wing-Kuen],
Zhao, H.M.[Hui-Min],
Marshall, S.[Stephen],
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust
Framework for Hyperspectral Image Classification,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link
1802
BibRef
Deng, C.[Cheng],
Liu, X.L.[Xiang-Long],
Li, C.[Chao],
Tao, D.C.[Da-Cheng],
Active multi-kernel domain adaptation for hyperspectral image
classification,
PR(77), 2018, pp. 306-315.
Elsevier DOI
1802
Active learning, Multi-kernel, Domain adaptation,
Hyperspectral image classification, Remote sensing
BibRef
Bhardwaj, K.[Kaushal],
Patra, S.[Swarnajyoti],
An unsupervised technique for optimal feature selection in attribute
profiles for spectral-spatial classification of hyperspectral images,
PandRS(138), 2018, pp. 139-150.
Elsevier DOI
1804
Attribute profile, Feature selection, Genetic algorithms,
Hyperspectral image, Mutual information, Remote sensing, Support vector machine
BibRef
Paul, S.[Subir],
Kumar, D.N.[D. Nagesh],
Spectral-spatial classification of hyperspectral data with mutual
information based segmented stacked autoencoder approach,
PandRS(138), 2018, pp. 265-280.
Elsevier DOI
1804
Hyperspectral remote sensing, Spectral-spatial classification,
Mutual information, Autoencoder, Support vector machine, Random forest
BibRef
He, L.,
Li, J.,
Liu, C.,
Li, S.,
Recent Advances on Spectral-Spatial Hyperspectral Image
Classification: An Overview and New Guidelines,
GeoRS(56), No. 3, March 2018, pp. 1579-1597.
IEEE DOI
1804
geophysical image processing, hyperspectral imaging,
image classification, bilayer-dependency,
spectral-spatial classification
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,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link
1806
BibRef
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,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link
1808
BibRef
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.
DOI Link
1808
BibRef
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,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link
1908
BibRef
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,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
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.Q.[Qiao-Qiao],
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,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link
1810
BibRef
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.
DOI Link
1902
BibRef
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.
DOI Link
1902
BibRef
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.
DOI Link
2303
BibRef
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,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link
1904
BibRef
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,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link
1904
BibRef
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.
DOI Link
1905
BibRef
Leng, Q.M.[Qing-Ming],
Yang, H.[Haiou],
Jiang, J.J.[Jun-Jun],
Label Noise Cleansing with Sparse Graph for Hyperspectral Image
Classification,
RS(11), No. 9, 2019, pp. xx-yy.
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,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
BibRef
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,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
BibRef
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
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,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link
1910
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
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
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
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
BibRef
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
BibRef
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.Z.[Pu-Zhao],
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
BibRef
Zhu, K.Q.[Kai-Qiang],
Chen, Y.S.[Yu-Shi],
Ghamisi, P.[Pedram],
Jia, X.P.[Xiu-Ping],
Benediktsson, J.A.[Jón Atli],
Deep Convolutional Capsule Network for Hyperspectral Image Spectral
and Spectral-Spatial Classification,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
1902
BibRef
He, X.[Xin],
Chen, Y.S.[Yu-Shi],
Lin, Z.H.[Zhou-Han],
Spatial-Spectral Transformer for Hyperspectral Image Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Huang, L.B.[Ling-Bo],
Chen, Y.S.[Yu-Shi],
He, X.[Xin],
Spectral-Spatial Mamba for Hyperspectral Image Classification,
RS(16), No. 13, 2024, pp. 2449.
DOI Link
2407
BibRef
Jia, S.[Sen],
Lin, Z.J.[Zhi-Jie],
Xu, M.[Meng],
Huang, Q.[Qiang],
Zhou, J.[Jun],
Jia, X.P.[Xiu-Ping],
Li, Q.Q.[Qing-Quan],
A Lightweight Convolutional Neural Network for Hyperspectral Image
Classification,
GeoRS(59), No. 5, May 2021, pp. 4150-4163.
IEEE DOI
2104
Hyperspectral imaging, Feature extraction, Convolution,
Convolutional neural networks, Deep learning,
hyperspectral imagery
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
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
BibRef
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
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
BibRef
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
BibRef
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
BibRef
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
Yu, Y.[Yang],
Ma, Y.[Yong],
Mei, X.G.[Xiao-Guang],
Fan, F.[Fan],
Huang, J.[Jun],
Ma, J.Y.[Jia-Yi],
A Spatial-Spectral Feature Descriptor for Hyperspectral Image
Matching,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link
2112
BibRef
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
BibRef
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,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link
2204
BibRef
Liang, N.N.[Nan-Nan],
Duan, P.[Puhong],
Xu, H.F.[Hai-Feng],
Cui, L.[Lin],
Multi-View Structural Feature Extraction for Hyperspectral Image
Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Fu, Y.[Ying],
Zhang, T.[Tao],
Wang, L.Z.[Li-Zhi],
Huang, H.[Hua],
Coded Hyperspectral Image Reconstruction Using Deep External and
Internal Learning,
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,
CVPR19(8024-8033).
IEEE DOI
2002
BibRef
Praveen, B.[Bishwas],
Menon, V.[Vineetha],
Dual-Branch-AttentionNet: A Novel Deep-Learning-Based
Spatial-Spectral Attention Methodology for Hyperspectral Data
Analysis,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Wang, A.[Aili],
Xing, S.[Shuang],
Zhao, Y.[Yan],
Wu, H.B.[Hai-Bin],
Iwahori, Y.[Yuji],
A Hyperspectral Image Classification Method Based on Adaptive
Spectral Spatial Kernel Combined with Improved Vision Transformer,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Ma, A.L.[Ai-Long],
Zhong, Y.F.[Yan-Fei],
Zhang, L.P.[Liang-Pei],
Spectral-Spatial Clustering with a Local Weight Parameter
Determination Method for Remote Sensing Imagery,
RS(8), No. 2, 2016, pp. 124.
DOI Link
1603
BibRef
Zhang, Y.S.[Yong-Shan],
Wang, Y.[Yang],
Chen, X.H.[Xiao-Hong],
Jiang, X.W.[Xin-Wei],
Zhou, Y.C.[Yi-Cong],
Spectral-Spatial Feature Extraction With Dual Graph Autoencoder for
Hyperspectral Image Clustering,
CirSysVideo(32), No. 12, December 2022, pp. 8500-8511.
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.X.[Ruo-Xi],
Parallel Spectral-Spatial Attention Network with Feature
Redistribution Loss for Hyperspectral Change Detection,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Huang, Y.X.[Yi-Xiang],
Zhang, L.[Lifu],
Qi, W.C.[Wen-Chao],
Huang, C.P.[Chang-Ping],
Song, R.X.[Ruo-Xi],
Contrastive Self-Supervised Two-Domain Residual Attention Network
with Random Augmentation Pool for Hyperspectral Change Detection,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Li, M.S.[Ming-Song],
Liu, Y.K.[Yi-Kun],
Xue, G.[Guangkuo],
Huang, Y.[Yuwen],
Yang, G.P.[Gong-Ping],
Exploring the Relationship Between Center and Neighborhoods: Central
Vector Oriented Self-Similarity Network for Hyperspectral Image
Classification,
CirSysVideo(33), No. 4, April 2023, pp. 1979-1993.
IEEE DOI
2304
Feature extraction, Task analysis,
Representation learning, Image classification,
efficient spectral-spatial feature learning
BibRef
Zhang, J.S.[Jun-San],
Zhao, L.[Li],
Jiang, H.Z.[Hong-Zhao],
Shen, S.[Shigen],
Wang, J.[Jian],
Zhang, P.Y.[Pei-Ying],
Zhang, W.[Wei],
Wang, L.Q.[Lei-Quan],
Hyperspectral Image Classification Based on Dense Pyramidal
Convolution and Multi-Feature Fusion,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link
2307
BibRef
Chen, Y.H.[Yu-Han],
Yan, Q.Y.[Qing-Yun],
Huang, W.M.[Wei-Min],
MSSFF: Advancing Hyperspectral Classification through Higher-Accuracy
Multistage Spectral-Spatial Feature Fusion,
RS(15), No. 24, 2023, pp. 5717.
DOI Link
2401
BibRef
Kang, J.F.[Jian-Fang],
Zhang, Y.N.[Yao-Nan],
Liu, X.C.[Xin-Chao],
Cheng, Z.X.[Zhong-Xin],
Hyperspectral Image Classification Using Spectral-Spatial
Double-Branch Attention Mechanism,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Bai, X.T.[Xiao-Tian],
Qi, B.[Biao],
Jin, L.X.[Long-Xu],
Li, G.N.[Guo-Ning],
Li, J.[Jin],
Fast and Accurate Hyperspectral Image Classification with Window
Shape Adaptive Singular Spectrum Analysis,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Li, S.P.[Shi-Ping],
Liang, L.[Lianhui],
Zhang, S.Q.[Shao-Quan],
Zhang, Y.[Ying],
Plaza, A.[Antonio],
Wang, X.[Xuehua],
End-to-End Convolutional Network and Spectral-Spatial Transformer
Architecture for Hyperspectral Image Classification,
RS(16), No. 2, 2024, pp. 325.
DOI Link
2402
BibRef
Zhang, Z.B.[Zhen-Bei],
Wang, S.[Shuo],
Zhang, W.L.[Wei-Lin],
Dilated Spectral-Spatial Gaussian Transformer Net for Hyperspectral
Image Classification,
RS(16), No. 2, 2024, pp. 287.
DOI Link
2402
BibRef
Li, S.[Sheng],
Wang, M.W.[Ming-Wei],
Cheng, C.[Chong],
Gao, X.J.[Xian-Jun],
Ye, Z.W.[Zhi-Wei],
Liu, W.[Wei],
Spectral-Spatial-Sensorial Attention Network with Controllable
Factors for Hyperspectral Image Classification,
RS(16), No. 7, 2024, pp. 1253.
DOI Link
2404
BibRef
Gao, Y.H.[Yun-Hao],
Li, W.[Wei],
Wang, J.J.[Jun-Jie],
Zhang, M.M.[Meng-Meng],
Tao, R.[Ran],
Relationship Learning From Multisource Images via Spatial-Spectral
Perception Network,
IP(33), 2024, pp. 3271-3284.
IEEE DOI
2405
Feature extraction, Soft sensors, Remote sensing, Land surface,
Graphical models, Distribution functions, Laser radar,
convolutional neural networks (CNN)
BibRef
Wang, M.[Minhui],
Sun, Y.X.[Ya-Xiu],
Xiang, J.H.[Jian-Hong],
Sun, R.[Rui],
Zhong, Y.[Yu],
Adaptive Learnable Spectral-Spatial Fusion Transformer for
Hyperspectral Image Classification,
RS(16), No. 11, 2024, pp. 1912.
DOI Link
2406
BibRef
Zhang, X.Z.[Xi-Zhen],
Zhang, A.[Aiwu],
Sun, Y.[Yuan],
Wang, J.[Juan],
Pang, H.Y.[Hai-Yang],
Peng, J.B.[Jin-Bang],
Chen, Y.S.[Yun-Sheng],
Zhang, J.X.[Jia-Xin],
Giannico, V.[Vincenzo],
Legesse, T.G.[Tsegaye Gemechu],
Shao, C.L.[Chang-Liang],
Xin, X.P.[Xiao-Ping],
Deep Multi-Order Spatial-Spectral Residual Feature Extractor for Weak
Information Mining in Remote Sensing Imagery,
RS(16), No. 11, 2024, pp. 1957.
DOI Link
2406
BibRef
Hong, D.,
Yao, J.,
Wu, X.,
Chanussot, J.,
Zhu, X.,
Spatial-spectral Manifold Embedding of Hyperspectral Data,
ISPRS20(B3:423-428).
DOI Link
2012
BibRef
Dovletov, G.[Gurbandurdy],
Hegemann, T.[Tobias],
Pauli, J.[Josef],
Spectral-Spatial Hyperspectral Image Classification Using Cascaded
Convolutional Neural Networks,
SCIA19(78-89).
Springer DOI
1906
BibRef
Han, D.,
Du, Q.,
Younan, N.H.,
Semisupervised classification of hyperspectral remote sensing images
with spatial majority voting,
PRRS16(1-4)
IEEE DOI
1704
hyperspectral imaging
BibRef
Franchi, G.,
Angulo, J.,
A deep spatial/spectral descriptor of hyperspectral texture using
scattering transform,
ICIP16(3568-3572)
IEEE DOI
1610
Hyperspectral imaging
BibRef
Franchi, G.,
Angulo, J.,
Sejdinovic, D.,
Hyperspectral image classification with support vector machines on
kernel distribution embeddings,
ICIP16(1898-1902)
IEEE DOI
1610
Hilbert space
BibRef
Zhong, P.,
Gong, Z.Q.[Zhi-Qiang],
Schönlieb, C.B.,
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)
IEEE DOI
1512
composite kernel; hyperspectral classification; nearest neighbor
BibRef
Huang, R.[Rui],
He, W.Y.[Wen-Yong],
Using tri-training to exploit spectral and spatial information for
hyperspectral data classification,
CVRS12(30-33).
IEEE DOI
1302
BibRef
Schaum, A.P.,
Stocker, A.,
Advanced algorithms for autonomous hyperspectral change detection,
AIPR04(33-38).
IEEE DOI
0410
BibRef
Schaum, A.P.,
Algorithms with attitude,
AIPR10(1-6).
IEEE DOI
1010
BibRef
Schaum, A.P.,
Advanced hyperspectral detection based on elliptically contoured
distribution models and operator feedback,
AIPR09(1-5).
IEEE DOI
0910
BibRef
Schaum, A.P.,
Adapting to Change:
The CFAR Problem in Advanced Hyperspectral Detection,
AIPR07(15-21).
IEEE DOI
0710
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multi-Scale, Spectral-Spatial Classification, Spatial-Spectral, Hyperspectral Data .