14.2.2.4.1 Hyperspectral Data, Neural Networks for Classification

Chapter Contents (Back)
Hyperspectral. Neural Networks. CNN.

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Bayes methods, Markov processes, convolution, feedforward neural nets, gradient methods, hyperspectral imaging, convolutional neural networks BibRef

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Training, Deep learning, Feature extraction, Labeling, Contracts, Hyperspectral imaging, Active learning (AL), Markov random field (MRF) BibRef

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Graph Convolutional Networks for Hyperspectral Image Classification,
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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,
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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,
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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,
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IEEE DOI 1809
Hyperspectral imaging, Machine learning, Kernel, Context modeling, Convolution, Task analysis, Convolutional neural network (CNN), hyperspectral image BibRef

Liu, B., Yu, X., Zhang, P., Yu, A., Fu, Q., Wei, X.,
Supervised Deep Feature Extraction for Hyperspectral Image Classification,
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IEEE DOI 1804
Euclidean distance, Feature extraction, Hyperspectral imaging, Support vector machines, Training, support vector machine (SVM) 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,
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Song, W., Li, S., Fang, L., Lu, T.,
Hyperspectral Image Classification With Deep Feature Fusion Network,
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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],
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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],
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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,
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Hyperspectral imaging, Training, Feature extraction, Task analysis, Knowledge engineering, Testing, Image edge detection, metalearning BibRef

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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.,
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Li, R.[Rui], Zheng, S.Y.[Shun-Yi], Duan, C.X.[Chen-Xi], Yang, Y.[Yang], Wang, X.[Xiqi],
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Bera, S.[Somenath], Shrivastava, V.K.[Vimal K.],
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Niu, B.Q.[Bing-Qing], Lan, J.H.[Jin-Hui], Shao, Y.[Yang], Zhang, H.[Hui],
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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,
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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,
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Xue, Z.X.[Zhi-Xiang],
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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],
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Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data,
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Li, R.[Rui], Pan, Z.B.[Zhi-Bin], Wang, Y.[Yang], Wang, P.[Ping],
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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],
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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],
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Masarczyk, W.[Wojciech], Glomb, P.[Przemyslaw], Grabowski, B.[Bartosz], Ostaszewski, M.[Mateusz],
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Hu, Y.[Yina], An, R.[Ru], Wang, B.[Benlin], Xing, F.[Fei], Ju, F.[Feng],
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Dang, L.X.[Lan-Xue], Pang, P.D.[Pei-Dong], Lee, J.[Jay],
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Dang, L.X.[Lan-Xue], Pang, P.D.[Pei-Dong], Zuo, X.Y.[Xian-Yu], Liu, Y.[Yang], Lee, J.[Jay],
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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.
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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

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
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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
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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.[Yuhao], Liu, W.[Wenyi], Feng, L.[Liuyan], Gao, W.J.[Wan-Jia],
Improved Transformer Net for Hyperspectral Image Classification,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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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
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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
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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
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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
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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
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Zhu, D.[Dehui], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Two-Stream Convolutional Networks for Hyperspectral Target Detection,
GeoRS(59), No. 8, August 2021, pp. 6907-6921.
IEEE DOI 2108
Hyperspectral imaging, Object detection, Detectors, Training, Dictionaries, Convolutional neural networks, two-stream networks 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.[Liqin],
Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning,
RS(13), No. 15, 2021, pp. xx-yy.
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Lin, J.Z.[Jian-Zhe], Mou, L.[Lichao], 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
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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.
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Kong, Y.[Yi], Wang, X.[Xuesong], 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
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He, X.[Xin], Chen, Y.[Yushi],
Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
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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.
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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

Gao, Y.L.[Yan-Long], Feng, Y.[Yan], Yu, X.[Xumin],
Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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
BibRef

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.[Meiping], Liu, C.[Caiyu], 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, Computer architecture, Data mining, Computational modeling, spectral feature extraction BibRef

Xu, Y.M.[Yi-Min], Li, Z.[Zhaokui], Li, W.[Wei], Du, Q.[Qian], Liu, C.[Cuiwei], 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.[Qiaoqiao], 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.[Zebin], 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
BibRef

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


Suárez, P.L.[Patricia L.], Sappa, A.D.[Angel D.], Vintimilla, B.X.[Boris X.],
Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation,
ICIP21(2783-2787)
IEEE DOI 2201
Image processing, Vegetation mapping, Estimation, Channel estimation, Generative adversarial networks, Sensors, instance normalization BibRef

Teffahi, H., Teffahi, N.,
EMAP-DCNN: A Novel Mathematical Morphology and Deep Learning Combined Framework for Hyperspectral Image Classification,
ISPRS20(B3:479-486).
DOI Link 2012
BibRef

Buehler, C., Schenkel, F., Gross, W., Schaab, G., Middelmann, W.,
Strategic Optimization of Convolutional Neural Networks For Hyperspectral Land Cover Classification,
ISPRS20(B3:363-369).
DOI Link 2012
BibRef

Voulodimos, A., Fokeas, K., Doulamis, N., Doulamis, A., Makantasis, K.,
Noise-tolerant Hyperspectral Image Classification Using Discrete Cosine Transform and Convolutional Neural Networks,
ISPRS20(B2:1281-1287).
DOI Link 2012
BibRef

Hosseiny, B., Rastiveis, H., Daneshtalab, S.,
Hyperspectral Image Classification By Exploiting Convolutional Neural Networks,
SMPR19(535-540).
DOI Link 1912
BibRef

Le, J.H.[Justin H.], Yazdanpanah, A.P.[Ali Pour], Regentova, E.E.[Emma E.], Muthukumar, V.[Venkatesan],
A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data,
ISVC15(I: 682-692).
Springer DOI 1601
BibRef

Li, T.[Tong], Zhang, J.P.[Jun-Ping], Zhang, Y.[Ye],
Classification of hyperspectral image based on deep belief networks,
ICIP14(5132-5136)
IEEE DOI 1502
Accuracy BibRef

Muhammed, H.H.,
Unsupervised hyperspectral image segmentation using a new class of neuro-fuzzy systems based on weighted incremental neural networks,
AIPR02(171-177).
IEEE DOI 0210
BibRef
And:
Using hyperspectral reflectance data for discrimination between healthy and diseased plants, and determination of damage-level in diseased plants,
AIPR02(49-54).
IEEE DOI 0210
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Spectral-Spatial Classification, Spatial-Spectral, Hyperspectral Data .


Last update:Jan 24, 2022 at 14:35:49