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Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information.
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2019
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2020
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2020
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2020
2020
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2020
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2020
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C. Zhao, B. Qin, S. Feng, W. Zhu, W. Sun, W. Li, and X. Jia, “Hyperspectral image classification with multi-attention transformer and adaptive superpixel segmentation-based active learning,” IEEE Transactions on Image Processing , 2023
2023
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Z. Qiu, J. Xu, J. Peng, and W. Sun, “Cross-channel dynamic spatial-spectral fusion transformer for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
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W. Zhou, S.-I. Kamata, H. Wang, and X. Xue, “Multiscanning-based rnn-transformer for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
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D. Hong, B. Zhang, X. Li, Y. Li, C. Li, J. Yao, N. Yokoya, H. Li, P. Ghamisi, X. Jia et al. , “Spectralgpt: Spectral remote sensing foundation model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
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2020
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A. Gu, T. Dao, S. Ermon, A. Rudra, and C. Ré, “Hippo: Recurrent memory with optimal polynomial projections,” Advances in neural information processing systems , vol. 33, pp. 1474–1487, 2020
2020
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D. Hong, Z. Han, J. Yao, L. Gao, B. Zhang, A. Plaza, and J. Chanussot, “Spectralformer: Rethinking hyperspectral image classification with transformers,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–15, 2021
2021
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X. Zhang, S. Shang, X. Tang, J. Feng, and L. Jiao, “Spectral partitioning residual network with spatial attention mechanism for hyperspectral image classification,” IEEE transactions on geoscience and remote sensing , vol. 60, pp. 1–14, 2021
2021
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X. He, Y. Chen, and Z. Lin, “Spatial-spectral transformer for hyperspectral image classification,” Remote Sensing , vol. 13, no. 3, p. 498, 2021
2021
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A. Gu, K. Goel, and C. Re, “Efficiently modeling long sequences with structured state spaces,” in International Conference on Learning Representations , 2021
2021
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A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. Ré, “Combining recurrent, convolutional, and continuous-time models with linear state space layers,” Advances in neural information processing systems , vol. 34, pp. 572–585, 2021
2021
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Y. Dong, Q. Liu, B. Du, and L. Zhang, “Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification,” IEEE Transactions on Image Processing , vol. 31, pp. 1559–1572, 2022
2022
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L. Sun, G. Zhao, Y. Zheng, and Z. Wu, “Spectral–spatial feature tokenization transformer for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2022
2022
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L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision mamba: Efficient visual representation learning with bidirectional state space model,” in Forty-first International Conference on Machine Learning , 2024
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M. Jiang, Y. Su, L. Gao, A. Plaza, X.-L. Zhao, X. Sun, and G. Liu, “Graphgst: Graph generative structure-aware transformer for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing , 2024
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