Few-shot learning (FSL) provides a promising solution for reducing annotation cost in hyperspectral image change detection (HSI-CD). However, most existing FSL approaches focus on single-modality or homogeneous domains, limiting their ability to handle domain shift. Moreover, many feature extractors rely on fixed kerne...
Hong-Min Gao, Shu-Yu Fei, Shu-Fang Xu et al.· IEEE Transactions on Image P...· 0 citations
Hyperspectral images (HSIs) possess fine spectral resolution. They can capture continuous and detailed spectral curves of ground objects, providing rich information for accurate classification. However, real-world scenes commonly suffer from diverse ground object morphology, spectral variability, and insufficient spati...
Shu-Fang Xu, Wei-Wen Xu, Shu-Yu Fei et al.· IEEE Transactions on Geoscie...· 0 citations
A state space modeling framework is introduced into bitemporal hyperspectral analysis to efficiently model long-range spectral–spatial dependencies and discriminative temporal interactions with linear computational complexity in HSI-CD.
Xiao-Dong Wei, Rui-Zhe Liu, Ji-Yuan Li et al.· IEEE Journal of Selected Top...· 0 citations
Deep learning-based fusion of hyperspectral images (HSI) and LiDAR has achieved strong performance in multimodal remote sensing classification, but its success is heavily constrained by the high cost of pixel-wise annotation. In extremely label-scarce regimes, such as 2-5 labeled samples per class, conventional deep mo...
Yiyan Zhang, Hongmin Gao, Weiping Ding et al.· IEEE Transactions on Image P...· 0 citations
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