Hyperspectral image classification requires a compact representation that can jointly capture discriminative spectral cues, local spatial structures, and efficient inference behavior under limited annotations. Existing CNN-based methods provide effective local pattern extraction but are less capable of modeling long-ra...
Xiao-Fei Yang, Rui-Ying Zhang, Wei-Jia Cao et al.· IEEE Transactions on Geoscie...· 0 citations
Hyperspectral image (HSI) classification remains challenging because accurate recognition requires both global contextual modeling and computationally efficient processing of high-dimensional spectral–spatial data. Transformer-based methods can capture long-range interactions, but their quadratic token-mixing cost limi...
Lu Zhou, Ji-Yan Li, Xiao-Fei Yang et al.· IEEE Geoscience and Remote S...· 0 citations
Tri-Domain Balanced Spectral–Spatial Attention (TBSA), a compact frequency-aware framework for HSIC, projects intermediate features into one-dimensional spectral, two-dimensional spatial, and three-dimensional spectral–spatial discrete cosine transform (DCT) domains, and it introduces a deformable frequency filter to a...
Hyperspectral image classification (HSIC) relies on effective modeling of coupled spectral–spatial interactions. While recent CNN-, Transformer-, and Mamba-based methods have improved feature extraction, most of them organize token interaction through layerwise aggregation and repeated stacking, leaving cross-layer pro...
Zihang Luo, Xiao-Fei Yang, Fei Yu et al.· IEEE Transactions on Geoscie...· 0 citations
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