14.2.2.4.1 Hyperspectral Data, Neural Networks for Classification

Chapter Contents (Back)
Hyperspectral. Neural Networks. CNN.
See also Semi-Supervised Clustering Applied to Hyperspectral Data.
See also Hyperspectral Target Detection.

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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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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.[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],
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Li, J.J.[Jiao-Jiao], Xi, B.[Bobo], Li, Y.S.[Yun-Song], Du, Q.[Qian], Wang, K.[Keyan],
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Song, W., Li, S., Fang, L., Lu, T.,
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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,
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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],
Hyperspectral Image Classification Based on Two-Phase Relation Learning Network,
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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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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,
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convolutional neural nets, data analysis, feature extraction, hyperspectral imaging, image classification, visual attention BibRef

Akbari, D.[Davood],
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Li, R.[Rui], Pan, Z.B.[Zhi-Bin], Wang, Y.[Yang], Wang, P.[Ping],
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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],
Shape Adaptive Neighborhood Information-Based Semi-Supervised Learning for Hyperspectral Image Classification,
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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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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],
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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

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,
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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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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,
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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,
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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],
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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,
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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,
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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],
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Diakite, A.[Alou], Gui, J.S.[Jiang-Sheng], Fu, X.P.[Xia-Ping],
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Ahmad, M.[Muhammad], Mazzara, M.[Manuel], Distefano, S.[Salvatore],
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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,
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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,
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Liu, J.[Jing], Li, Y.[Yang], Zhao, F.[Feng], Liu, Y.[Yi],
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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,
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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,
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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],
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Shi, H.[Hao], Cao, G.[Guo], Zhang, Y.Q.[You-Qiang], Ge, Z.X.[Zi-Xian], Liu, Y.B.[Yan-Bo], Fu, P.[Peng],
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Kong, Y.[Yi], Wang, X.S.[Xue-Song], Cheng, Y.[Yuhu], Chen, C.L.P.[C. L. Philip],
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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
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, 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.[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.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
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

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
DOI Link 2303
BibRef

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.
DOI Link 2304
BibRef

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

Liang, L.H.[Lian-Hui], Zhang, S.Q.[Shao-Quan], Li, J.[Jun], Plaza, A.[Antonio], Cui, Z.[Zhi],
Multi-Scale Spectral-Spatial Attention Network for Hyperspectral Image Classification Combining 2D Octave and 3D Convolutional Neural Networks,
RS(15), No. 7, 2023, pp. 1758.
DOI Link 2304
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
BibRef

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
BibRef

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
BibRef

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
BibRef

Ashraf, M.[Mahmood], Zhou, X.[Xichuan], 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
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:Mar 16, 2024 at 20:36:19