14.2.2.4 Spectral-Spatial Classification, Hyperspectral Data

Chapter Contents (Back)
Hyperspectral. Spectral-Spatial.

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

Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.,
Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles,
GeoRS(43), No. 3, March 2005, pp. 480-491.
IEEE Abstract. 0501
See also Multisource remote sensing data classification based on consensus and pruning. BibRef

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

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

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

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

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

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

Zhang, H., Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Li, P.,
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

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

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

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

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

Zhang, H.Y.[Hong-Yan], Zhai, H.[Han], Liao, W.[Wenzhi], Cao, L.[Liqin], 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

Zhang, Y.X.[Yu-Xiang], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Liu, T.L.[Tong-Liang],
Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection,
GeoRS(55), No. 2, February 2017, pp. 894-906.
IEEE DOI 1702
geophysical image processing BibRef

Zhang, Y.X.[Yu-Xiang], Wu, K.[Ke], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Hu, X.Y.[Xiang-Yun],
Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
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.[Tianyi], 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 BibRef

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

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

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

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

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

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

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

Kang, X.D.[Xu-Dong], Xiang, X., Li, S.T.[Shu-Tao], Benediktsson, J.A.[Jón Atli],
PCA-Based Edge-Preserving Features for Hyperspectral Image Classification,
GeoRS(55), No. 12, December 2017, pp. 7140-7151.
IEEE DOI 1712
Feature extraction, Hyperspectral imaging, Image edge detection, Principal component analysis, Support vector machines, support vector machine (SVM) BibRef

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

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Benediktsson, J.A.[Jón Atli],
Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images,
GeoRS(53), No. 4, April 2015, pp. 2241-2253.
IEEE DOI 1502
feature extraction 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

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

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

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

Dian, R.[Renwei], 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

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

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

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

Shen, L.L.[Lin-Lin], Jia, S.[Sen],
Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification,
GeoRS(49), No. 12, December 2011, pp. 5039-5046.
IEEE DOI 1201
BibRef

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

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

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

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

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

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

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

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

Othman, H., Qian, S.E.,
Noise Reduction of Hyperspectral Imagery Using Hybrid Spatial-Spectral Derivative-Domain Wavelet Shrinkage,
GeoRS(44), No. 2, February 2006, pp. 397-408.
IEEE DOI 0602
BibRef

Liu, J., Lu, W.,
A Probabilistic Framework for Spectral-Spatial Classification of Hyperspectral Images,
GeoRS(54), No. 9, September 2016, pp. 5375-5384.
IEEE DOI 1609
estimation theory BibRef

Li, J., Bioucas-Dias, J.M., Plaza, A.,
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.,
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., Bioucas-Dias, J.M., Plaza, A.,
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

Ji, R.R.[Rong-Rong], Gao, Y.[Yue], Hong, R.C.[Ri-Chang], Liu, Q.[Qiong], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Spectral-Spatial Constraint Hyperspectral Image Classification,
GeoRS(52), No. 3, March 2014, pp. 1811-1824.
IEEE DOI 1403
hyperspectral imaging BibRef

Chen, C.[Chen], Li, W.[Wei], Su, H.J.[Hong-Jun], Liu, K.[Kui],
Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine,
RS(6), No. 6, 2014, pp. 5795-5814.
DOI Link 1407
BibRef

Li, W.[Wei], Chen, C.[Chen], Su, H.J.[Hong-Jun], Du, Q.,
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification,
GeoRS(53), No. 7, July 2015, pp. 3681-3693.
IEEE DOI 1503
Educational institutions BibRef

Li, W.[Wei], Wu, G.D.[Guo-Dong], Zhang, F.[Fan], Du, Q.[Qian],
Hyperspectral Image Classification Using Deep Pixel-Pair Features,
GeoRS(55), No. 2, February 2017, pp. 844-853.
IEEE DOI 1702
hyperspectral imaging BibRef

Li, H.[Hong], Song, Y.[Yalong], Chen, C.L.P.[C.L. Philip],
Hyperspectral Image Classification Based on Multiscale Spatial Information Fusion,
GeoRS(55), No. 9, September 2017, pp. 5302-5312.
IEEE DOI 1709
hyperspectral imaging, image classification, image fusion, HSI classification, L1-DE, MSIF, hyperspectral image classification, local 1D embedding, multiscale spatial information fusion, multiscale strategy, BibRef

Wei, Y.[Yantao], Zhou, Y.[Yicong], Li, H.[Hong],
Spectral-Spatial Response for Hyperspectral Image Classification,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Zhou, Y.[Yicong], Wei, Y.[Yantao],
Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification,
Cyber(46), No. 7, July 2016, pp. 1667-1678.
IEEE DOI 1606
Accuracy 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

He, L.[Lin], Li, Y.Q.[Yuan-Qing], Li, X.X.[Xiao-Xin], Wu, W.[Wei],
Spectral-Spatial Classification of Hyperspectral Images via Spatial Translation-Invariant Wavelet-Based Sparse Representation,
GeoRS(53), No. 5, May 2015, pp. 2696-2712.
IEEE DOI 1502
geophysical image processing BibRef

Liu, J.J.[Jian-Jun], Xiao, Z.Y.[Zhi-Yong], Chen, Y.F.[Yu-Feng], Yang, J.L.[Jin-Long],
Spatial-Spectral Graph Regularized Kernel Sparse Representation for Hyperspectral Image Classification,
IJGI(6), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Sun, L.[Le], Wu, Z.[Zebin], Liu, J.J.[Jian-Jun], Xiao, L.[Liang], Wei, Z.H.[Zhi-Hui],
Supervised Spectral-Spatial Hyperspectral Image Classification With Weighted Markov Random Fields,
GeoRS(53), No. 3, March 2015, pp. 1490-1503.
IEEE DOI 1402
Markov processes BibRef

Shen, Y.[Yu], Chen, J.Y.[Jian-Yu], Xiao, L.[Liang],
Supervised classification of hyperspectral images using local-receptive-fields-based kernel extreme learning machine,
ICIP17(3120-3124)
IEEE DOI 1803
Convolution, Hyperspectral imaging, Kernel, Mathematical model, Neurons, Principal component analysis, LRF-KELM, SVM, random convolution nodes BibRef

Sun, L.[Le], Wu, Z.[Zebin], Wei, Z.H.[Zhi-Hui], Liu, J.J.[Jian-Jun], Li, X.X.[Xing-Xiu],
Supervised Hyperspectral Image Classification Combining Sparse Unmixing and Spatial Constraint,
CVRS12(110-115).
IEEE DOI 1302
BibRef

He, L.[Lin], Li, J.[Jun], Plaza, A.[Antonio], Li, Y.Q.[Yuan-Qing],
Discriminative Low-Rank Gabor Filtering for Spectral-Spatial Hyperspectral Image Classification,
GeoRS(55), No. 3, March 2017, pp. 1381-1395.
IEEE DOI 1703
Feature extraction BibRef

Hu, J.[Jie], He, Z.[Zhi], Li, J.[Jun], He, L.[Lin], Wang, Y.[Yiwen],
3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian, H., Bioucas-Dias, J.M., Li, X.,
Spectral-Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization,
GeoRS(52), No. 10, October 2014, pp. 6298-6314.
IEEE DOI 1407
Hyperspectral imaging BibRef

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

Li, J.[Jun], Plaza, A., Bioucas-Dias, J.M.,
Integration of Hyperspectral Image Classification and Unmixing for Active Learning,
ISIDF11(1-4).
IEEE DOI 1111
BibRef

Liu, J.J.[Jian-Jun], Wu, Z.[Zebin], Li, J.[Jun], Plaza, A.[Antonio], Yuan, Y.H.[Yun-Hao],
Probabilistic-Kernel Collaborative Representation for Spatial-Spectral Hyperspectral Image Classification,
GeoRS(54), No. 4, April 2016, pp. 2371-2384.
IEEE DOI 1604
Adaptation models BibRef

Liu, J.J.[Jian-Jun], Wu, Z.[Zebin], Xiao, Z.Y.[Zhi-Yong], Yang, J.L.[Jin-Long],
Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares,
IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link 1712
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).
HTML Version. 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.[Xiaoyu], 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

Jiao, L., Liang, M., Chen, H., Yang, S., Liu, H., Cao, X.,
Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification,
GeoRS(55), No. 10, October 2017, pp. 5585-5599.
IEEE DOI 1710
convolution, feature extraction, hyperspectral imaging, image fusion, neural nets, statistics, HSIC, deep fully convolutional network, BibRef

He, M., Li, B., Chen, H.,
Multi-scale 3D deep convolutional neural network for hyperspectral image classification,
ICIP17(3904-3908)
IEEE DOI 1803
Convolution, Feature extraction, Hyperspectral imaging, Kernel, Training, multi-scale BibRef

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., Dikshit, O.,
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

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

Zhang, J.[Jing], Chen, L.[Lu], Zhuo, L.[Li], Liang, X.[Xi], Li, J.[Jiafeng],
An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
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

Yang, L., Wang, M., Yang, S., Zhao, H., Jiao, L., Feng, X.,
Hybrid Probabilistic Sparse Coding With Spatial Neighbor Tensor for Hyperspectral Imagery Classification,
GeoRS(56), No. 5, May 2018, pp. 2491-2502.
IEEE DOI 1805
Encoding, Hyperspectral imaging, Probabilistic logic, Tensile stress, Training, Uncertainty, Hybrid probabilistic, tensor sparse coding 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

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

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.[Wenju], Dou, S.G.[Shu-Guang], Jiang, Z.M.[Zhong-Min], Sun, L.[Liujie],
A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
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

Ahmad, O.[Ola], Collet, C.[Christophe], Salzenstein, F.[Fabien],
Spatio-spectral Gaussian random field modeling approach for target detection on hyperspectral data obtained in very low SNR,
ICIP15(2090-2094)
IEEE DOI 1512
Expected Euler-characteristic 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

Schaum, A.P.,
Autonomous Hyperspectral Target Detection with Quasi-Stationarity Violation at Background Boundaries,
AIPR06(16-16).
IEEE DOI 0610
BibRef
Earlier:
Hyperspectral detection algorithms: operational, next generation, on the horizon,
AIPR05(72-80).
IEEE DOI 0510
BibRef
Earlier:
Matched affine joint subspace detection in remote hyperspectral reconnaissance,
AIPR02(13-18).
IEEE DOI 0210
BibRef

Schaum, A.P.,
Bayesian solutions to non-Bayesian detection problems: Unification through fusion,
AIPR14(1-4)
IEEE DOI 1504
Bayes methods BibRef

Schaum, A.P.,
Data association for fusion in spatial and spectral imaging,
AIPR03(87-92).
IEEE DOI 0310
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data, Endmember Extraction .


Last update:Aug 16, 2018 at 18:22:30