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, Xiaofei Yang et al.· IEEE Geoscience and Remote S...· 0 citations
Hyperspectral image classification (HSIC) requires a classifier to distinguish land-cover categories from densely sampled spectral signatures while preserving the spatial arrangement of local materials. Although convolutional networks, Transformer architectures, and recent state-space models have greatly improved spect...
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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