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

Chapter Contents (Back)
Hyperspectral. Spectral-Spatial. Spatial-Spectral.
See also Hyperspectral Target Detection.

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], Kĺsen, I.[Ingebjřrg], Skauli, T.[Torbjřrn],
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
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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
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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
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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,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
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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.
DOI Link 1808
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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,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
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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,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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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.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
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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.
DOI Link 1902
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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.
DOI Link 1902
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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.
DOI Link 2303
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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,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
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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,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
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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.
DOI Link 1905
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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,
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,
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DOI Link 1907
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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.
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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
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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
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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
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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
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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
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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
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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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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
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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
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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
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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
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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
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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
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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
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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
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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
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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,
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
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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


Zhou, W.L.[Wei-Lian], Kamata, S.I.[Sei-Ichiro], Luo, Z.B.[Zheng-Bo], Chen, X.Y.[Xiao-Yue],
Hierarchical Unified Spectral-Spatial Aggregated Transformer for Hyperspectral Image Classification,
ICPR22(3041-3047)
IEEE DOI 2212
Training, Computational modeling, Transformers, Convolutional neural networks, Information exchange 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 .


Last update:Jul 13, 2024 at 15:27:21