14.2.2.4.1 Hyperspectral Data, Neural Networks for Classification

Jimenez, L.O., Morales-Morell, A., Creus, A.,
Classification of Hyperdimensional Data Based on Feature and Decision Fusion Approaches Using Projection Pursuit, Majority Voting, and Neural Networks,
GeoRS(37), No. 3, May 1999, pp. 1360.
IEEE Top Reference. BibRef 9905

Ratle, F., Camps-Valls, G., Weston, J.,
Semisupervised Neural Networks for Efficient Hyperspectral Image Classification,
GeoRS(48), No. 5, May 2010, pp. 2271-2282.
IEEE DOI 1006
BibRef

Eddy, P.R., Smith, A.M., Hill, B.D., Peddle, D.R., Coburn, C.A., Blackshaw, R.E.,
Hybrid Segmentation: Artificial Neural Network Classification of High Resolution Hyperspectral Imagery for Site-Specific Herbicide Management in Agriculture,
PhEngRS(74), No. 10, October 2008, pp. 1249-1258.
WWW Link. 0804
A new, efficient AI method is presented for improved weed management in crops with significant economic and environmental advantages. BibRef

Rebetez, J.[Julien], Tuia, D.[Devis], Courty, N.[Nicolas],
Network-Based Correlated Correspondence for Unsupervised Domain Adaptation of Hyperspectral Satellite Images,
ICPR14(3921-3926)
IEEE DOI 1412
Approximation algorithms. Adapt to changes in data distribution. BibRef

Zhong, P.[Ping], Gong, Z.Q.[Zhi-Qiang], Li, S.T.[Shu-Tao], Schönlieb, C.B.[Carola-Bibiane],
Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification,
GeoRS(55), No. 6, June 2017, pp. 3516-3530.
IEEE DOI 1706
BibRef
Earlier: A1, A2, A4, Only:
A Diversified Deep Belief Network For Hyperspectral Image Classification,
ISPRS16(B7: 443-449).
DOI Link 1610
Feature extraction, Hidden Markov models, Hyperspectral imaging, Neurons, Training, Deep belief network (DBN), diversity, hyperspectral image, image, classification BibRef

Singhal, V.[Vanika], Aggarwal, H.K.[Hemant K.], Tariyal, S.[Snigdha], Majumdar, A.[Angshul],
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification,
GeoRS(55), No. 9, September 2017, pp. 5274-5283.
IEEE DOI 1709
deep belief network, hyperspectral image classification, linear classifier, BibRef

Goel, A.[Anurag], Majumdar, A.[Angshul],
Semi-Supervised Graphical Deep Dictionary Learning for Hyperspectral Image Classification From Limited Samples,
ICIP24(2108-2114)
IEEE DOI 2411
Training data, Imaging, Machine learning, Benchmark testing, Hyperspectral imaging, Image classification, Hyperspectral Imaging BibRef

Singh, S.[Shikha], Majumdar, A.[Angshul], Chouzenoux, E.[Emilie], Chierchia, G.[Giovanni],
Semi-supervised Deep Convolutional Transform Learning for Hyperspectral Image Classification,
ICIP22(206-210)
IEEE DOI 2211
Measurement, Transforms, Machine learning, Information filters, Hyperspectral imaging, Image classification, deep learning BibRef

Cao, X.Y.[Xiang-Yong], Zhou, F.[Feng], Xu, L.[Lin], Meng, D.Y.[De-Yu], Xu, Z.B.[Zong-Ben], Paisley, J.[John],
Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network,
IP(27), No. 5, May 2018, pp. 2354-2367.
IEEE DOI 1804
Bayes methods, Markov processes, convolution, feedforward neural nets, gradient methods, hyperspectral imaging, convolutional neural networks BibRef

Cao, X.Y.[Xiang-Yong], Yao, J.[Jing], Xu, Z.B.[Zong-Ben], Meng, D.Y.[De-Yu],
Hyperspectral Image Classification With Convolutional Neural Network and Active Learning,
GeoRS(58), No. 7, July 2020, pp. 4604-4616.
IEEE DOI 2006
Training, Deep learning, Feature extraction, Labeling, Contracts, Hyperspectral imaging, Active learning (AL), Markov random field (MRF) BibRef

Hong, D.F.[Dan-Feng], Gao, L.R.[Lian-Ru], Yao, J.[Jing], Zhang, B.[Bing], Plaza, A.[Antonio], Chanussot, J.[Jocelyn],
Graph Convolutional Networks for Hyperspectral Image Classification,
GeoRS(59), No. 7, July 2021, pp. 5966-5978.
IEEE DOI 2106
Feature extraction, Convolution, Hyperspectral imaging, Task analysis, Symmetric matrices, Fourier transforms, fusion BibRef

Hao, S., Wang, W., Ye, Y., Nie, T., Bruzzone, L.,
Two-Stream Deep Architecture for Hyperspectral Image Classification,
GeoRS(56), No. 4, April 2018, pp. 2349-2361.
IEEE DOI 1804
Feature extraction, Hyperspectral imaging, Machine learning, Training, Class-specific fusion, two-stream architecture BibRef

Hao, S., Wang, W., Ye, Y., Li, E., Bruzzone, L.,
A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss,
GeoRS(56), No. 8, August 2018, pp. 4650-4663.
IEEE DOI 1808
feature extraction, geophysical image processing, hyperspectral imaging, image classification, image resolution, super-resolution (SR) BibRef

Yang, X.F.[Xiao-Fei], Ye, Y.M.[Yun-Ming], Li, X.T.[Xu-Tao], Lau, R.Y.K.[Raymond Y. K.], Zhang, X.F.[Xiao-Feng], Huang, X.H.[Xiao-Hui],
Hyperspectral Image Classification With Deep Learning Models,
GeoRS(56), No. 9, September 2018, pp. 5408-5423.
IEEE DOI 1809
Hyperspectral imaging, Machine learning, Kernel, Context modeling, Convolution, Task analysis, Convolutional neural network (CNN), hyperspectral image BibRef

Liu, B.[Bing], Yu, X.C.[Xu-Chu], Zhang, P.Q.[Peng-Qiang], Yu, A.Z.[An-Zhu], Fu, Q.Y.[Qiong-Ying], Wei, X.P.[Xiang-Po],
Supervised Deep Feature Extraction for Hyperspectral Image Classification,
GeoRS(56), No. 4, April 2018, pp. 1909-1921.
IEEE DOI 1804
Euclidean distance, Feature extraction, Hyperspectral imaging, Support vector machines, Training, support vector machine (SVM) BibRef

Liu, B.[Bing], Gao, K.L.[Kui-Liang], Yu, A.Z.[An-Zhu], Ding, L.[Lei], Qiu, C.P.[Chun-Ping], Li, J.[Jia],
ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Li, J.J.[Jiao-Jiao], Xi, B.[Bobo], Li, Y.S.[Yun-Song], Du, Q.[Qian], Wang, K.[Keyan],
Hyperspectral Classification Based on Texture Feature Enhancement and Deep Belief Networks,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Song, W., Li, S., Fang, L., Lu, T.,
Hyperspectral Image Classification With Deep Feature Fusion Network,
GeoRS(56), No. 6, June 2018, pp. 3173-3184.
IEEE DOI 1806
Convolutional neural networks, Feature extraction, Hyperspectral imaging, Logistics, Support vector machines, residual learning BibRef

Ma, X.R.[Xiao-Rui], Fu, A.[Anyan], Wang, J.[Jie], Wang, H.Y.[Hong-Yu], Yin, B.C.[Bao-Cai],
Hyperspectral Image Classification Based on Deep Deconvolution Network With Skip Architecture,
GeoRS(56), No. 8, August 2018, pp. 4781-4791.
IEEE DOI 1808
feedforward neural nets, geophysical image processing, image classification, image representation, hyperspectral image classification BibRef

Ma, X.R.[Xiao-Rui], Ji, S.[Sheng], Wang, J.[Jie], Geng, J.[Jie], Wang, H.Y.[Hong-Yu],
Hyperspectral Image Classification Based on Two-Phase Relation Learning Network,
GeoRS(57), No. 12, December 2019, pp. 10398-10409.
IEEE DOI 1912
Hyperspectral imaging, Training, Measurement, Deep learning, Task analysis, Classification, deep network, hyperspectral image BibRef

Ma, X.R.[Xiao-Rui], Ji, S.[Sheng], Wang, J.[Jie], Liu, X.K.[Xiao-Kai], Wang, H.Y.[Hong-Yu],
Classification of Hyperspectral Image Based on Task-Specific Learning Network,
GeoRS(59), No. 10, October 2021, pp. 8646-8656.
IEEE DOI 2109
Hyperspectral imaging, Training, Feature extraction, Task analysis, Knowledge engineering, Testing, Image edge detection, metalearning BibRef

Xu, M.[Meng], Zhao, Y.Y.[Yuan-Yuan], Liang, Y.J.[Ya-Jun], Ma, X.R.[Xiao-Rui],
Hyperspectral Image Classification Based on Class-Incremental Learning with Knowledge Distillation,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Hamouda, M.[Maissa], Ettabaa, K.S.[Karim Saheb], Bouhlel, M.S.[Med Salim],
Hyperspectral imaging classification based on convolutional neural networks by adaptive sizes of windows and filters,
IET-IPR(13), No. 2, February 2019, pp. 392-398.
DOI Link 1902
BibRef

Wu, P.[Peida], Cui, Z.G.[Zi-Guan], Gan, Z.L.[Zong-Liang], Liu, F.[Feng],
Residual Group Channel and Space Attention Network for Hyperspectral Image Classification,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Chen, C.[Chen], Ma, Y.[Yi], Ren, G.B.[Guang-Bo],
A Convolutional Neural Network with Fletcher-Reeves Algorithm for Hyperspectral Image Classification,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Hamouda, M.[Maissa], Ettabaa, K.S.[Karim Saheb], Bouhlel, M.S.[Med Salim],
Framework for Automatic Selection of Kernels based on Convolutional Neural Networks and CkMeans Clustering Algorithm,
IJIG(19), No. 4 2019, pp. 1950019.
DOI Link 2001
BibRef

Hamouda, M.[Maissa], Ettabaa, K.S.[Karim Saheb], Bouhlel, M.S.[Med Salim],
Smart feature extraction and classification of hyperspectral images based on convolutional neural networks,
IET-IPR(14), No. 10, August 2020, pp. 1999-2005.
DOI Link 2008
BibRef

Wang, L., Zhang, T., Fu, Y., Huang, H.,
HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging,
IP(28), No. 5, May 2019, pp. 2257-2270.
IEEE DOI 1903
convolutional neural nets, data compression, hyperspectral imaging, image coding, image reconstruction, hyperspectral image reconstruction BibRef

Haut, J.M., Paoletti, M.E., Plaza, J., Plaza, A., Li, J.,
Visual Attention-Driven Hyperspectral Image Classification,
GeoRS(57), No. 10, October 2019, pp. 8065-8080.
IEEE DOI 1910
convolutional neural nets, data analysis, feature extraction, hyperspectral imaging, image classification, visual attention BibRef

Akbari, D.[Davood],
Improved neural network classification of hyperspectral imagery using weighted genetic algorithm and hierarchical segmentation,
IET-IPR(13), No. 12, October 2019, pp. 2169-2175.
DOI Link 1911
BibRef

Akbari, D., Moradizadeh, M., Akbari, M.,
Spectral-spatial Classification of Hyperspectral Imagery Using Neural Network Algorithm and Hierarchical Segmentation,
PTVSBB19(1-5).
DOI Link 1912
BibRef

Akbari, D., Moradizadeh, M.,
Spectral-spatial Classification of Hyperspectral Imagery Using a Hybrid Framework,
SMPR19(41-44).
DOI Link 1912
BibRef

Akbari, D.[Davood], Safari, A.R.,
Rule-Based Classification of a Hyperspectral Image Using MSSC Hierarchical Segmentation,
SMPR13(13-18).
DOI Link 1311
BibRef

Niazmardi, S., Safari, A.R., Homayouni, S.,
Maximum Margin Clustering of Hyperspectral Data,
SMPR13(305-308).
DOI Link 1311
BibRef

Paoletti, M.E., Haut, J.M., Plaza, J., Plaza, A.,
Deep learning classifiers for hyperspectral imaging: A review,
PandRS(158), 2019, pp. 279-317.
Elsevier DOI 1912
Survey, Hyperspectral Imaging. Deep learning (DL), Hyperspectral imaging (HSI), Earth observation (EO), Classification BibRef

Li, R.[Rui], Zheng, S.Y.[Shun-Yi], Duan, C.X.[Chen-Xi], Yang, Y.[Yang], Wang, X.Q.[Xi-Qi],
Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Bera, S.[Somenath], Shrivastava, V.K.[Vimal K.],
Effect of pooling strategy on convolutional neural network for classification of hyperspectral remote sensing images,
IET-IPR(14), No. 3, 28 February 2020, pp. 480-486.
DOI Link 2002
BibRef

Niu, B.Q.[Bing-Qing], Lan, J.H.[Jin-Hui], Shao, Y.[Yang], Zhang, H.[Hui],
A Dual-Branch Extraction and Classification Method Under Limited Samples of Hyperspectral Images Based on Deep Learning,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Zhang, C.Y.[Cheng-Ye], Yue, J.[Jun], Qin, Q.M.[Qi-Ming],
Deep Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Zhao, W.Z.[Wen-Zhi], Chen, X.[Xi], Chen, J.G.[Jia-Ge], Qu, Y.[Yang],
Sample Generation with Self-Attention Generative Adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Xue, Z.X.[Zhi-Xiang],
Semi-supervised convolutional generative adversarial network for hyperspectral image classification,
IET-IPR(14), No. 4, 27 March 2020, pp. 709-719.
DOI Link 2003
BibRef

Sildir, H.[Hasan], Aydin, E.[Erdal], Kavzoglu, T.[Taskin],
Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Li, Z.K.[Zhao-Kui], Tang, X.Y.[Xiang-Yi], Li, W.[Wei], Wang, C.Y.[Chuan-Yun], Liu, C.[Cuiwei], He, J.R.[Jin-Rong],
A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Song, A.[Ahram], Kim, Y.I.[Yong-Il],
Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Le Moan, S.[Steven], Cariou, C.[Claude],
Minimax Bridgeness-Based Clustering for Hyperspectral Data,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Li, R.[Rui], Pan, Z.B.[Zhi-Bin], Wang, Y.[Yang], Wang, P.[Ping],
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification,
GeoRS(58), No. 5, May 2020, pp. 3136-3147.
IEEE DOI 2005
Convolutional neural network (CNN), dimension reduction, feature extraction, hyperspectral image (HSI) classification, mapping layers BibRef

Li, J.[Jun], Lin, D.[Daoyu], Wang, Y.[Yang], Xu, G.L.[Guang-Luan], Zhang, Y.Y.[Yun-Yan], Ding, C.B.[Chi-Biao], Zhou, Y.H.[Yan-Hai],
Deep Discriminative Representation Learning with Attention Map for Scene Classification,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Zheng, Z., Zhong, Y., Ma, A., Zhang, L.,
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification,
GeoRS(58), No. 8, August 2020, pp. 5612-5626.
IEEE DOI 2007
Field programmable gate arrays, Decoding, Feature extraction, Semantics, Training, Hyperspectral imaging, Feature fusion, patch-free global learning BibRef

Yang, M.D.[Ming-Der], Huang, K.H.[Kai-Hsiang], Tsai, H.P.[Hui-Ping],
Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Masarczyk, W.[Wojciech], Glomb, P.[Przemyslaw], Grabowski, B.[Bartosz], Ostaszewski, M.[Mateusz],
Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Hu, Y.[Yina], An, R.[Ru], Wang, B.[Benlin], Xing, F.[Fei], Ju, F.[Feng],
Shape Adaptive Neighborhood Information-Based Semi-Supervised Learning for Hyperspectral Image Classification,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Dang, L.X.[Lan-Xue], Pang, P.D.[Pei-Dong], Lee, J.[Jay],
Depth-Wise Separable Convolution Neural Network with Residual Connection for Hyperspectral Image Classification,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Dang, L.X.[Lan-Xue], Pang, P.D.[Pei-Dong], Zuo, X.Y.[Xian-Yu], Liu, Y.[Yang], Lee, J.[Jay],
A Dual-Path Small Convolution Network for Hyperspectral Image Classification,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Rangnekar, A.[Aneesh], Mokashi, N.[Nilay], Ientilucci, E.J.[Emmett J.], Kanan, C.[Christopher], Hoffman, M.J.[Matthew J.],
AeroRIT: A New Scene for Hyperspectral Image Analysis,
GeoRS(58), No. 11, November 2020, pp. 8116-8124.
IEEE DOI 2011
Hyperspectral imaging, Sensors, Semantics, Automobiles, Training, Task analysis, hyperspectral imaging, image segmentation, supervised learning BibRef

Deng, C.[Chubo], Cen, Y.[Yi], Zhang, L.[Lifu],
Learning-Based Hyperspectral Imagery Compression through Generative Neural Networks,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Zheng, J., Feng, Y., Bai, C., Zhang, J.,
Hyperspectral Image Classification Using Mixed Convolutions and Covariance Pooling,
GeoRS(59), No. 1, January 2021, pp. 522-534.
IEEE DOI 2012
Feature extraction, Hyperspectral imaging, Data mining, Kernel, principal component analysis (PCA) BibRef

Ma, C.[Chen], Jiang, J.J.[Jun-Jun], Li, H.Y.[Hua-Yi], Mei, X.G.[Xiao-Guang], Bai, C.C.[Cheng-Chao],
Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Liang, H.B.[Hong-Bo], Bao, W.X.[Wen-Xing], Shen, X.F.[Xiang-Fei],
Adaptive Weighting Feature Fusion Approac Based on Generative Adversarial Network for Hyperspectral Image Classification,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Hang, R.L.[Ren-Long], Li, Z.[Zhu], Liu, Q.S.[Qing-Shan], Ghamisi, P.[Pedram], Bhattacharyya, S.S.[Shuvra S.],
Hyperspectral Image Classification With Attention-Aided CNNs,
GeoRS(59), No. 3, March 2021, pp. 2281-2293.
IEEE DOI 2103
Feature extraction, Hyperspectral imaging, Adaptation models, Training, Task analysis, Data mining, Attention modules, weighted fusion BibRef

Yan, L., Zhao, M., Wang, X., Zhang, Y., Chen, J.,
Object Detection in Hyperspectral Images,
SPLetters(28), 2021, pp. 508-512.
IEEE DOI 2103
Hyperspectral imaging, Feature extraction, Object detection, Training, Kernel, Task analysis, Spatial resolution, convolutional neural network BibRef

Shi, H.[Hao], Cao, G.[Guo], Ge, Z.X.[Zi-Xian], Zhang, Y.Q.[You-Qiang], Fu, P.[Peng],
Double-Branch Network with Pyramidal Convolution and Iterative Attention for Hyperspectral Image Classification,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Ge, Z.X.[Zi-Xian], Cao, G.[Guo], Zhang, Y.Q.[You-Qiang], Shi, H.[Hao], Liu, Y.B.[Yan-Bo], Shafique, A.[Ayesha], Fu, P.[Peng],
Subpixel Multilevel Scale Feature Learning and Adaptive Attention Constraint Fusion for Hyperspectral Image Classification,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Sun, J.G.[Jin-Guang], Wang, W.L.[Wan-Li], Wei, X.[Xian], Fang, L.[Li], Tang, X.L.[Xiao-Liang], Xu, Y.S.[Yu-Sheng], Yu, H.[Hui], Yao, W.[Wei],
Deep Clustering With Intraclass Distance Constraint for Hyperspectral Images,
GeoRS(59), No. 5, May 2021, pp. 4135-4149.
IEEE DOI 2104
Clustering algorithms, Feature extraction, Hyperspectral imaging, Task analysis, Sensors, Neural networks, Deep learning, remote sensing BibRef

Zhai, H.[Han], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
Nonlocal Means Regularized Sketched Reweighted Sparse and Low-Rank Subspace Clustering for Large Hyperspectral Images,
GeoRS(59), No. 5, May 2021, pp. 4164-4178.
IEEE DOI 2104
Dictionaries, Computational modeling, Hyperspectral imaging, Sparse matrices, Clustering methods, Clustering algorithms, sparse and low-rank representation (SLRR) BibRef

Wang, J.J.[Jun-Jie], Gao, F.[Feng], Dong, J.Y.[Jun-Yu], Du, Q.[Qian],
Adaptive DropBlock-Enhanced Generative Adversarial Networks for Hyperspectral Image Classification,
GeoRS(59), No. 6, June 2021, pp. 5040-5053.
IEEE DOI 2106
Generative adversarial networks, Generators, Training, Feature extraction, Shape, Hyperspectral sensors, hyperspectral image (HSI) classification BibRef

Qing, Y.H.[Yu-Hao], Liu, W.Y.[Wen-Yi], Feng, L.Y.[Liu-Yan], Gao, W.J.[Wan-Jia],
Improved Transformer Net for Hyperspectral Image Classification,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Liang, X.J.[Xue-Jian], Zhang, Y.[Ye], Zhang, J.P.[Jun-Ping],
Attention Symbiotic Neural Network for Hyperspectral Image Refined Classification Based on Relative Water Content Retrieval,
GeoRS(59), No. 7, July 2021, pp. 5998-6016.
IEEE DOI 2106
Feature extraction, Machine learning, Data models, Data mining, Hyperspectral imaging, Biological system modeling, relative water content retrieval (RWCR) BibRef

Gong, H.[Hang], Li, Q.X.[Qiu-Xia], Li, C.L.[Chun-Lai], Dai, H.S.[Hai-Shan], He, Z.P.[Zhi-Ping], Wang, W.J.[Wen-Jing], Li, H.Y.[Hao-Yang], Han, F.[Feng], Tuniyazi, A.[Abudusalamu], Mu, T.[Tingkui],
Multiscale Information Fusion for Hyperspectral Image Classification Based on Hybrid 2D-3D CNN,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Diakite, A.[Alou], Gui, J.S.[Jiang-Sheng], Fu, X.P.[Xia-Ping],
Hyperspectral image classification using 3D 2D CNN,
IET-IPR(15), No. 5, 2021, pp. 1083-1092.
DOI Link 2106
BibRef

Ahmad, M.[Muhammad], Mazzara, M.[Manuel], Distefano, S.[Salvatore],
Regularized CNN Feature Hierarchy for Hyperspectral Image Classification,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Zhang, C.Z.[Chao-Zi], Wang, J.L.[Jian-Li], Yao, K.[Kainan],
Global Random Graph Convolution Network for Hyperspectral Image Classification,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Liu, J.[Jing], Yang, Z.[Zhe], Liu, Y.[Yi], Mu, C.H.[Cai-Hong],
Hyperspectral Remote Sensing Images Deep Feature Extraction Based on Mixed Feature and Convolutional Neural Networks,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Liu, J.[Jing], Li, Y.[Yang], Zhao, F.[Feng], Liu, Y.[Yi],
Hyperspectral Remote Sensing Images Feature Extraction Based on Spectral Fractional Differentiation,
RS(15), No. 11, 2023, pp. 2879.
DOI Link 2306
BibRef

Wei, L.F.[Li-Fei], Wang, K.[Kun], Lu, Q.K.[Qi-Kai], Liang, Y.J.[Ya-Jing], Li, H.B.[Hai-Bo], Wang, Z.X.[Zheng-Xiang], Wang, R.[Run], Cao, L.Q.[Li-Qin],
Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Lin, J.Z.[Jian-Zhe], Mou, L.C.[Li-Chao], Zhu, X.X.[Xiao Xiang], Ji, X.Y.[Xiang-Yang], Wang, Z.J.[Z. Jane],
Attention-Aware Pseudo-3-D Convolutional Neural Network for Hyperspectral Image Classification,
GeoRS(59), No. 9, September 2021, pp. 7790-7802.
IEEE DOI 2109
Feature extraction, Solid modeling, Pipelines, Hyperspectral imaging, Convolution, Task analysis, Neural networks, transfer learning BibRef

Li, Z.W.[Zhong-Wei], Zhu, X.[Xue], Xin, Z.Q.[Zi-Qi], Guo, F.M.[Fang-Ming], Cui, X.S.[Xing-Shuai], Wang, L.Q.[Lei-Quan],
Variational Generative Adversarial Network with Crossed Spatial and Spectral Interactions for Hyperspectral Image Classification,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Ge, Z.X.[Zi-Xian], Cao, G.[Guo], Shi, H.[Hao], Zhang, Y.Q.[You-Qiang], Li, X.S.[Xue-Song], Fu, P.[Peng],
Compound Multiscale Weak Dense Network with Hybrid Attention for Hyperspectral Image Classification,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Shi, H.[Hao], Cao, G.[Guo], Zhang, Y.Q.[You-Qiang], Ge, Z.X.[Zi-Xian], Liu, Y.B.[Yan-Bo], Fu, P.[Peng],
H2A2Net: A Hybrid Convolution and Hybrid Resolution Network with Double Attention for Hyperspectral Image Classification,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Kong, Y.[Yi], Wang, X.S.[Xue-Song], Cheng, Y.[Yuhu], Chen, C.L.P.[C. L. Philip],
Multi-Stage Convolutional Broad Learning with Block Diagonal Constraint for Hyperspectral Image Classification,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

He, X.[Xin], Chen, Y.S.[Yu-Shi],
Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Guo, W.H.[Wen-Hui], Xu, G.X.[Gui-Xun], Liu, W.F.[Wei-Feng], Liu, B.D.[Bao-Di], Wang, Y.J.[Yan-Jiang],
CNN-Combined Graph Residual Network with Multilevel Feature Fusion for Hyperspectral Image Classification,
IET-CV(15), No. 8, 2021, pp. 592-607.
DOI Link 2110
convolutional neural networks, graph residual network, hyperspectral image classification, superpixel segmentation BibRef

Guo, W.H.[Wen-Hui], Ye, H.L.[Hai-Liang], Cao, F.L.[Fei-Long],
Feature-Grouped Network With Spectral-Spatial Connected Attention for Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
Feature extraction, Deep learning, Data mining, Principal component analysis, Hyperspectral imaging, Training, spectral attention BibRef

Xu, H.L.[Hui-Lin], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
A Superpixel Guided Sample Selection Neural Network for Handling Noisy Labels in Hyperspectral Image Classification,
GeoRS(59), No. 11, November 2021, pp. 9486-9503.
IEEE DOI 2111
Noise measurement, Training, Feature extraction, Hyperspectral imaging, Task analysis, Support vector machines, sample selection BibRef

Xue, Z.H.[Zhao-Hui], Zhang, M.X.[Meng-Xue], Liu, Y.F.[Yi-Feng], Du, P.J.[Pei-Jun],
Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification,
GeoRS(59), No. 11, November 2021, pp. 9600-9615.
IEEE DOI 2111
Feature extraction, Correlation, Optimization, Hyperspectral imaging, Structural engineering, second-order pooling BibRef

Zhang, Y.X.[Yu-Xiang], Li, W.[Wei], Tao, R.[Ran], Peng, J.T.[Jiang-Tao], Du, Q.[Qian], Cai, Z.Q.[Zhao-Quan],
Cross-Scene Hyperspectral Image Classification With Discriminative Cooperative Alignment,
GeoRS(59), No. 11, November 2021, pp. 9646-9660.
IEEE DOI 2111
Manifolds, Hyperspectral imaging, Support vector machines, Training, Task analysis, Learning systems, Correlation, Cross-scene, subspace alignment (SA) BibRef

Feng, Y.C.[Yu-Chao], Zheng, J.W.[Jian-Wei], Qin, M.J.[Meng-Jie], Bai, C.[Cong], Zhang, J.L.[Jing-Lin],
3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Pereira, N.S.[Nuno S.], Plaza, J.[Javier], Plaza, A.[Antonio],
Ghostnet for Hyperspectral Image Classification,
GeoRS(59), No. 12, December 2021, pp. 10378-10393.
IEEE DOI 2112
Feature extraction, Data models, Computational modeling, Data mining, Convolution, Hyperspectral imaging, hyperspectral BibRef

Qu, Y.[Ying], Baghbaderani, R.K.[Razieh Kaviani], Li, W.[Wei], Gao, L.[Lianru], Zhang, Y.X.[Yu-Xiang], Qi, H.R.[Hai-Rong],
Physically Constrained Transfer Learning Through Shared Abundance Space for Hyperspectral Image Classification,
GeoRS(59), No. 12, December 2021, pp. 10455-10472.
IEEE DOI 2112
Feature extraction, Training, Data mining, Deep learning, Correlation, Labeling, Hyperspectral imaging, Deep learning, transfer learning BibRef

Zhao, J.L.[Jin-Ling], Hu, L.[Lei], Dong, Y.Y.[Ying-Ying], Huang, L.S.[Lin-Sheng],
Hybrid Dense Network with Dual Attention for Hyperspectral Image Classification,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Xi, B.[Bobo], Li, J.J.[Jiao-Jiao], Li, Y.S.[Yun-Song], Song, R.[Rui], Xiao, Y.C.[Yu-Chao], Shi, Y.Z.[Yan-Zi], Du, Q.[Qian],
Multi-Direction Networks With Attentional Spectral Prior for Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Feature extraction, Data mining, Training, Support vector machines, Hyperspectral imaging, Data models, multi-direction BibRef

Yu, C.Y.[Chun-Yan], Han, R.[Rui], Song, M.P.[Mei-Ping], Liu, C.Y.[Cai-Yu], Chang, C.I.[Chein-I],
Feedback Attention-Based Dense CNN for Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Feature extraction, Training, Hyperspectral imaging, Data mining, Computational modeling, spectral feature extraction BibRef

Xu, Y.M.[Yi-Min], Li, Z.K.[Zhao-Kui], Li, W.[Wei], Du, Q.[Qian], Liu, C.W.[Cui-Wei], Fang, Z.Q.[Zhuo-Qun], Zhai, L.[Lin],
Dual-Channel Residual Network for Hyperspectral Image Classification With Noisy Labels,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Noise measurement, Feature extraction, Training, Robustness, Deep learning, Task analysis, Noise robustness, noisy labels BibRef

Cui, B.[Benlei], Dong, X.M.[Xue-Mei], Zhan, Q.Q.[Qiao-Qiao], Peng, J.T.[Jiang-Tao], Sun, W.W.[Wei-Wei],
LiteDepthwiseNet: A Lightweight Network for Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Convolution, Standards, Hyperspectral imaging, Training, Solid modeling, Computational modeling, Data models, lightweight network BibRef

Wang, J.[Jue], Chen, H.[He], Ma, L.[Long], Chen, L.[Liang], Gong, X.D.[Xiao-Dong], Liu, W.C.[Wen-Chao],
Sphere Loss: Learning Discriminative Features for Scene Classification in a Hyperspherical Feature Space,
GeoRS(60), 2022, pp. 1-19.
IEEE DOI 2112
Feature extraction, Remote sensing, Measurement, Training, Task analysis, Deep learning, Sports, Deep learning, sphere loss BibRef

Liu, Q.[Qian], Wu, Z.B.[Ze-Bin], Jia, X.P.[Xiu-Ping], Xu, Y.[Yang], Wei, Z.H.[Zhi-Hui],
From Local to Global: Class Feature Fused Fully Convolutional Network for Hyperspectral Image Classification,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Kumar, V.[Vinod], Singh, R.S.[Ravi Shankar], Dua, Y.[Yaman],
Morphologically dilated convolutional neural network for hyperspectral image classification,
SP:IC(101), 2022, pp. 116549.
Elsevier DOI 2201
Mathematical morphology, Hyperspectral image (HSI), Dilated convolution, Binarization, Convolutional neural network (CNN) BibRef

Pande, S.[Shivam], Banerjee, B.[Biplab],
HyperLoopNet: Hyperspectral image classification using multiscale self-looping convolutional networks,
PandRS(183), 2022, pp. 422-438.
Elsevier DOI 2201
Hyperspectral images, Image classification, Convolutional neural networks, Feedback networks BibRef

Wang, Q.Y.[Qing-Yan], Chen, M.[Meng], Zhang, J.P.[Jun-Ping], Kang, S.Q.[Shou-Qiang], Wang, Y.J.[Yu-Jing],
Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Praveen, B.[Bishwas], Menon, V.[Vineetha],
A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Dong, Y.[Yanni], Liu, Q.W.[Quan-Wei], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification,
IP(31), 2022, pp. 1559-1572.
IEEE DOI 2202
Feature extraction, Convolutional neural networks, Training, Hyperspectral imaging, Data mining, Decoding, Data models, attention mechanism BibRef

Wang, J.N.[Jia-Ning], Huang, R.[Runhu], Guo, S.Y.[Si-Ying], Li, L.H.[Lin-Hao], Pei, Z.[Zhao], Liu, B.[Bo],
HyperLiteNet: Extremely Lightweight Non-Deep Parallel Network for Hyperspectral Image Classification,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Chang, Y.L.[Yang-Lang], Tan, T.H.[Tan-Hsu], Lee, W.H.[Wei-Hong], Chang, L.[Lena], Chen, Y.N.[Ying-Nong], Fan, K.C.[Kuo-Chin], Alkhaleefah, M.[Mohammad],
Consolidated Convolutional Neural Network for Hyperspectral Image Classification,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Cao, J.M.[Jin-Ming], Li, Y.Y.[Yang-Yan], Sun, M.C.[Ming-Chao], Chen, Y.[Ying], Lischinski, D.[Dani], Cohen-Or, D.[Daniel], Chen, B.Q.[Bao-Quan], Tu, C.H.[Chang-He],
DO-Conv: Depthwise Over-Parameterized Convolutional Layer,
IP(31), 2022, pp. 3726-3736.
IEEE DOI 2206
Kernel, Convolution, Training, Tensors, Color, Over-parameterization, depthwise convolution BibRef

Zhao, C.H.[Chun-Hui], Zhu, W.X.[Wen-Xiang], Feng, S.[Shou],
Superpixel Guided Deformable Convolution Network for Hyperspectral Image Classification,
IP(31), 2022, pp. 3838-3851.
IEEE DOI 2206
Feature extraction, Convolution, Hyperspectral imaging, Data mining, Shape, Kernel, Deep learning, bilateral filter BibRef

Tang, H.J.[Hao-Jin], Li, Y.S.[Yan-Shan], Huang, Z.Q.[Zhi-Quan], Zhang, L.[Li], Xie, W.X.[Wei-Xin],
Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Zhang, T.X.[Tian-Xiang], Wang, W.X.[Wen-Xuan], Wang, J.[Jing], Cai, Y.X.[Yuan-Xiu], Yang, Z.F.[Zhi-Fang], Li, J.Y.[Jiang-Yun],
Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Huang, W.[Wei], Zhao, Z.B.[Zhuo-Bing], Sun, L.[Le], Ju, M.[Ming],
Dual-Branch Attention-Assisted CNN for Hyperspectral Image Classification,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Alkhatib, M.Q.[Mohammed Q.], Al-Saad, M.[Mina], Aburaed, N.[Nour], Almansoori, S.[Saeed], Zabalza, J.[Jaime], Marshall, S.[Stephen], Al-Ahmad, H.[Hussain],
Tri-CNN: A Three Branch Model for Hyperspectral Image Classification,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Di, X.[Xiyao], Xue, Z.H.[Zhao-Hui], Zhang, M.X.[Meng-Xue],
Active Learning-Driven Siamese Network for Hyperspectral Image Classification,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Li, B.[Bing], Wang, Q.W.[Qi-Wen], Liang, J.H.[Jia-Hong], Zhu, E.Z.[En-Ze], Zhou, R.Q.[Rong-Qian],
SquconvNet: Deep Sequencer Convolutional Network for Hyperspectral Image Classification,
RS(15), No. 4, 2023, pp. xx-yy.
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Liu, Q.Y.[Qiu-Yue], Fu, M.[Min], Liu, X.F.[Xue-Feng],
Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification,
RS(15), No. 7, 2023, pp. 1820.
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Liu, Q.Y.[Qiu-Yue], Liu, X.F.[Xue-Feng], Zhang, S.[Shan], Fu, M.[Min],
Hyperspectral Image Classification Based on Convolutional Neural Network Embedded with Attention Mechanism and Shadow Enhancement by Dynamic Stochastic Resonance,
ICIVC22(455-461)
IEEE DOI 2301
Deep learning, Solid modeling, Stochastic resonance, Feature extraction, Sensors, Convolutional neural networks, 3D CNN, DSR BibRef

Zhang, E.[Erlei], Zhang, J.Y.[Jia-Yi], Bai, J.X.[Jia-Xin], Bian, J.[Jiarong], Fang, S.[Shaoyi], Zhan, T.[Tao], Feng, M.C.[Ming-Chen],
Attention-Embedded Triple-Fusion Branch CNN for Hyperspectral Image Classification,
RS(15), No. 8, 2023, pp. 2150.
DOI Link 2305
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Wang, D.[Di], Zhang, J.[Jing], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng],
DCN-T: Dual Context Network With Transformer for Hyperspectral Image Classification,
IP(32), 2023, pp. 2536-2551.
IEEE DOI 2305
Feature extraction, Transformers, Image segmentation, Pipelines, Task analysis, Dimensionality reduction, Semantics, context capturing BibRef

Gong, Z.Q.[Zhi-Qiang], Zhong, P.[Ping], Yao, W.[Wen], Zhou, W.[Weien], Qi, J.H.[Jia-Hao], Hu, P.[Panhe],
A CNN with noise inclined module and denoise framework for hyperspectral image classification,
IET-IPR(17), No. 9, 2023, pp. 2575-2584.
DOI Link 2307
convolutional neural networks (CNNs), denoise framework, diversity, hyperspectral image classification, noise inclined module BibRef

Dahiya, N.[Neelam], Singh, S.[Sartajvir], Gupta, S.[Sheifali],
A Review on Deep Learning Classifier for Hyperspectral Imaging,
IJIG(23), No. 4 2023, pp. 2350036.
DOI Link 2308
Survey, Deep Learning. BibRef

Feng, H.[Hao], Wang, Y.C.[Yong-Cheng], Li, Z.[Zheng], Zhang, N.[Ning], Zhang, Y.X.[Yu-Xi], Gao, Y.[Yunxiao],
Information Leakage in Deep Learning-Based Hyperspectral Image Classification: A Survey,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
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Wang, Z.H.[Zhi-Hui], Cao, B.S.[Bai-Song], Liu, J.[Jun],
Hyperspectral Image Classification via Spatial Shuffle-Based Convolutional Neural Network,
RS(15), No. 16, 2023, pp. 3960.
DOI Link 2309
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Zhong, C.C.[Cheng-Cheng], Gong, N.[Na], Zhang, Z.T.[Zi-Tong], Jiang, Y.[Yanan], Zhang, K.[Kai],
LiteCCLKNet: A lightweight criss-cross large kernel convolutional neural network for hyperspectral image classification,
IET-CV(17), No. 7, 2023, pp. 763-776.
DOI Link 2310
criss-cross convolution, hyperspectral image classification, large kernel, lightweight network BibRef

Xu, T.[Tuo], Han, B.[Bing], Li, J.[Jie], Du, Y.F.[Yue-Fan],
Domain-Invariant Feature and Generative Adversarial Network Boundary Enhancement for Multi-Source Unsupervised Hyperspectral Image Classification,
RS(15), No. 22, 2023, pp. 5306.
DOI Link 2311
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Ashraf, M.[Mahmood], Zhou, X.C.[Xi-Chuan], Vivone, G.[Gemine], Chen, L.H.[Li-Hui], Chen, R.[Rong], Majdard, R.S.[Reza Seifi],
Spatial-Spectral BERT for Hyperspectral Image Classification,
RS(16), No. 3, 2024, pp. 539.
DOI Link 2402
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Wang, C.Z.[Chuan-Zhi], Huang, J.[Jun], Lv, M.Y.[Ming-Yun], Wu, Y.M.[Yong-Mei], Qin, R.[Ruiru],
Dual-Branch Adaptive Convolutional Transformer for Hyperspectral Image Classification,
RS(16), No. 9, 2024, pp. 1615.
DOI Link 2405
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Chen, Y.[Yuyi], Wang, X.P.[Xiao-Peng], Zhang, J.H.[Jia-Hua], Shang, X.D.[Xiao-Di], Hu, Y.[Yabin], Zhang, S.C.[Shi-Chao], Wang, J.J.[Jia-Jie],
A New Dual-Branch Embedded Multivariate Attention Network for Hyperspectral Remote Sensing Classification,
RS(16), No. 11, 2024, pp. 2029.
DOI Link 2406
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Zhang, H.[Heng], Liu, H.H.[Han-Hu], Yang, R.H.[Rong-Hao], Wang, W.[Wei], Luo, Q.Q.[Qing-Qu], Tu, C.D.[Chang-Da],
Hyperspectral Image Classification Based on Double-Branch Multi-Scale Dual-Attention Network,
RS(16), No. 12, 2024, pp. 2051.
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Islam, M.R.[Md. Rashedul], Islam, M.T.[Md. Touhid], Uddin, M.P.[Md Palash], Ulhaq, A.[Anwaar],
Improving Hyperspectral Image Classification with Compact Multi-Branch Deep Learning,
RS(16), No. 12, 2024, pp. 2069.
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Sun, Q.[Qian], Zhao, G.R.[Guang-Rui], Xia, X.Y.[Xin-Yuan], Xie, Y.[Yu], Fang, C.R.[Chen-Rong], Sun, L.[Le], Wu, Z.B.[Ze-Bin], Pan, C.S.[Cheng-Sheng],
Hyperspectral Image Classification Based on Multi-Scale Convolutional Features and Multi-Attention Mechanisms,
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Center-Highlighted Multiscale CNN for Classification of Hyperspectral Images,
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Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification,
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Using hyperspectral reflectance data for discrimination between healthy and diseased plants, and determination of damage-level in diseased plants,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Spectral-Spatial Classification, Spatial-Spectral, Hyperspectral Data .