14.2.2.4.1 Hyperspectral Data, Neural Networks for Classification

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

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Cloud computing, Neural networks, Hyperspectral imaging, Iron, Data compression, Autoencoder (AE), cloud computing, speedup BibRef

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Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification,
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A Diversified Deep Belief Network For Hyperspectral Image Classification,
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Feature extraction, Hidden Markov models, Hyperspectral imaging, Neurons, Training, Deep belief network (DBN), diversity, hyperspectral image, image, classification BibRef

Singhal, V., Aggarwal, H.K., Tariyal, S., Majumdar, A.,
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification,
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IEEE DOI 1709
deep belief network, hyperspectral image classification, linear classifier, BibRef

Zhong, Z.L.[Zi-Long], Li, J.[Jonathan], Luo, Z.M.[Zhi-Ming], Chapman, M.[Michael],
Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework,
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Feature extraction, Hyperspectral imaging, Machine learning, Robustness, Testing, Training, 3-D deep learning, spectral-spatial residual network (SSRN) BibRef

Cao, X.Y.[Xiang-Yong], Zhou, F.[Feng], Xu, L.[Lin], Meng, D.[Deyu], Xu, Z.B.[Zong-Ben], Paisley, J.[John],
Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network,
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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.[Deyu],
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

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., Yu, X., Zhang, P., Yu, A., Fu, Q., Wei, X.,
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

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,
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

Fang, L., Liu, G., Li, S., Ghamisi, P., Benediktsson, J.A.,
Hyperspectral Image Classification With Squeeze Multibias Network,
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convolutional neural nets, hyperspectral imaging, image classification, learning (artificial intelligence), squeeze multibias network (SMBN) 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,
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IEEE DOI 1808
feedforward neural nets, geophysical image processing, image classification, image representation, hyperspectral image classification BibRef

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Hyperspectral Image Classification Based on Two-Phase Relation Learning Network,
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IEEE DOI 1912
Hyperspectral imaging, Training, Measurement, Deep learning, Task analysis, Classification, deep network, hyperspectral image BibRef

Hamouda, M.[Maissa], Ettabaa, K.S.[Karim Saheb], Bouhlel, M.S.[Med Salim],
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Hamouda, M.[Maissa], Ettabaa, K.S.[Karim Saheb], Bouhlel, M.S.[Med Salim],
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Hamouda, M.[Maissa], Ettabaa, K.S.[Karim Saheb], Bouhlel, M.S.[Med Salim],
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Wang, L., Zhang, T., Fu, Y., Huang, H.,
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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.,
Visual Attention-Driven Hyperspectral Image Classification,
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IEEE DOI 1910
convolutional neural nets, data analysis, feature extraction, hyperspectral imaging, image classification, visual attention BibRef

Akbari, D.[Davood],
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Spectral-spatial Classification of Hyperspectral Imagery Using Neural Network Algorithm and Hierarchical Segmentation,
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Akbari, D., Moradizadeh, M.,
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Akbari, D.[Davood], Safari, A.R.,
Rule-Based Classification of a Hyperspectral Image Using MSSC Hierarchical Segmentation,
SMPR13(13-18).
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Niazmardi, S., Safari, A.R., Homayouni, S.,
Maximum Margin Clustering of Hyperspectral Data,
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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.[Xiqi],
Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network,
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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],
A Dual-Branch Extraction and Classification Method Under Limited Samples of Hyperspectral Images Based on Deep Learning,
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Zhang, C.[Chengye], 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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Sildir, H.[Hasan], Aydin, E.[Erdal], Kavzoglu, T.[Taskin],
Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization,
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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],
A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification,
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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,
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Le Moan, S.[Steven], Cariou, C.[Claude],
Minimax Bridgeness-Based Clustering for Hyperspectral Data,
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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,
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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],
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Wu, P.[Peida], Cui, Z.[Ziguan], Gan, Z.L.[Zong-Liang], Liu, F.[Feng],
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Zheng, Z., Zhong, Y., Ma, A., Zhang, L.,
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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],
Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification,
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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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Hosseiny, B., Rastiveis, H., Daneshtalab, S.,
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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,
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Springer DOI 1601
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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
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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
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Spectral-Spatial Classification, Hyperspectral Data .


Last update:Sep 21, 2020 at 13:40:48