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Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed.
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W. Zhao and S. Du, “Spectral-spatial feature extraction for hyperspectral image classification,”
2016
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Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,”
2016
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P. Liu, H. Zhang, and K. B. Eom, “Active deep learning for classification of hyperspectral images,”
2017
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P. Zhong, Z. Gong, S. Li, and C. Schönlieb, “Learning to diversify deep belief networks for hyperspectral image classification,”
2017
Cited alongside, same era.
L. Mou, P. Ghamisi, and X. X. Zhu, “Deep recurrent neural networks for hyperspectral image classification,”
2017
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H. Lee and H. Kwon, “Going deeper with contextual CNN for hyperspectral image classification,”
2017
Later among the works it cites.
T. Dundar and T. Ince, “Sparse representation-based hyperspectral image classification using multiscale superpixels and guided filter,”
2018
Closest in time.
S. Amini, S. Homayouni, A. Safari, and A. A. Darvishsefat, “Object-based classification of hyperspectral data using random forest algorithm,”
2018
Closest in time.
F. Li, D. A. Clausi, L. Xu, and A. Wong, “ST-IRGS: A region-based self-training algorithm applied to hyperspectral image classification and segmentation,”
2018
Closest in time.
Q. Gao, S. Lim, and X. Jia, “Hyperspectral image classification using convolutional neural networks and multiple feature learning,”
2018
Closest in time.
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A. Santara, K. Mani, P. Hatwar, A. Singh, A. Garg, K. Padia, and P. Mitra, “BASS Net: Band-adaptive spectral-spatial feature learning neural network for hyperspectral image classification,”
2017
Cited alongside, same era.
P. Ribalta, M. Marcinkiewicz, and J. Nalepa, “Segmentation of hyperspectral images using quantized convolutional neural networks,” in
2018
Closest in time.