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Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences.
R. E. Kalman, “A new approach to linear filtering and prediction problems,” 1960
1960
Earlier work this paper cites.
G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing
2005
Earlier work this paper cites.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2012
Earlier work this paper cites.
J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geoscience and Remote Sensing Magazine
2013
Earlier work this paper cites.
W. Ma, C. Gong, Y. Hu, P. Meng, and F. Xu, “The hughes phenomenon in hyperspectral classification based on the ground spectrum of grasslands in the region around qinghai lake,” in International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications
2013
Earlier work this paper cites.
G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, “Advances in hyperspectral image classification: Earth monitoring with statistical learning methods,” IEEE Signal Processing Magazine
2013
Earlier work this paper cites.
A. Samat, P. Du, S. Liu, J. Li, and L. Cheng, “ E 2 lms {{\rm E}^{2}}{\rm lms} : Ensemble extreme learning machines for hyperspectral image classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2014
Earlier work this paper cites.
J. M. Haut, M. E. Paoletti, J. Plaza, J. Li, and A. Plaza, “Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach,” IEEE Transactions on Geoscience and Remote Sensing
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Earlier work this paper cites.
J. Theiler, A. Ziemann, S. Matteoli, and M. Diani, “Spectral variability of remotely sensed target materials: Causes, models, and strategies for mitigation and robust exploitation,” IEEE Geoscience and Remote Sensing Magazine
2019
Earlier work this paper cites.
D. Hong, N. Yokoya, N. Ge, J. Chanussot, and X. X. Zhu, “Learnable manifold alignment (lema): A semi-supervised cross-modality learning framework for land cover and land use classification,” ISPRS Journal of Photogrammetry and Remote Sensing
2019
Earlier work this paper cites.
R. Hang, Q. Liu, D. Hong, and P. Ghamisi, “Cascaded recurrent neural networks for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing
2019
Cited alongside, same era.
J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Free-form image inpainting with gated convolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2019
Cited alongside, same era.
N. Audebert, B. Le Saux, and S. Lefèvre, “Deep learning for classification of hyperspectral data: A comparative review,” IEEE Geoscience and Remote Sensing Magazine
2019
Cited alongside, same era.
Y. Zhong, X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang, “Whu-hi: Uav-borne hyperspectral with high spatial resolution (h2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with crf,” Remote Sensing of Environment
2020
Cited alongside, same era.
DOI: 10.1109/TGRS.2022.3206208
J. Yao, X. Cao, D. Hong, X. Wu, D. Meng, J. Chanussot, and Z. Xu, “Semi-active convolutional neural networks for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing · 2022
Later among the works it cites.
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2023
Later among the works it cites.
DOI:10.1109/TGRS.2023.3279834
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Later among the works it cites.
Y. Xu, W. Yu, P. Ghamisi, M. Kopp, and S. Hochreiter, “Txt2img-mhn: Remote sensing image generation from text using modern hopfield networks,” IEEE Transactions on Image Processing
2023
Later among the works it cites.
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D. Hong, N. Yokoya, J. Chanussot, J. Xu, and X. X. Zhu, “Joint and progressive subspace analysis (jpsa) with spatial–spectral manifold alignment for semisupervised hyperspectral dimensionality reduction,” IEEE Transactions on Cybernetics
2021
Cited alongside, same era.
D. Hong, J. Hu, J. Yao, J. Chanussot, and X. X. Zhu, “Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model,” ISPRS Journal of Photogrammetry and Remote Sensing
2021
Cited alongside, same era.
Doi: 10.1109/TGRS.2021.3130716
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Cited alongside, same era.
B. Xi, J. Li, Y. Li, R. Song, D. Hong, and J. Chanussot, “Few-shot learning with class-covariance metric for hyperspectral image classification,” IEEE Transactions on Image Processing
2022
Cited alongside, same era.
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2022
Cited alongside, same era.
A. Gu, K. Goel, and C. Ré, “Efficiently modeling long sequences with structured state spaces,” International Conference on Learning Representations
2022
Cited alongside, same era.
E. Nguyen, K. Goel, A. Gu, G. Downs, P. Shah, T. Dao, S. Baccus, and C. Ré, “S4nd: Modeling images and videos as multidimensional signals with state spaces,” Advances in Neural Information Processing Systems
2022
Cited alongside, same era.
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10.1109/TGRS.2023.3284671
J. Yao, B. Zhang, C. Li, D. Hong, and J. Chanussot, “Extended vision transformer (exvit) for land use and land cover classification: A multimodal deep learning framework,” IEEE Transactions on Geoscience and Remote Sensing · 2023
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2023
Later among the works it cites.
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