SANet: A Self-Attention Network for Agricultural Hyperspectral Image Classification

Unlike conventional hyperspectral image (HSI) classification in general scenes, agricultural HSI classification poses greater challenges due to the increased occurrence of "same spectrum different object" and "different spectrum same object" phenomena caused by class similarities. Furthermore, the dense spatial distribution of land cover categories in agricultural scenes and the mixing of spatial–spectral features at crop boundaries add to the complexity of agricultural HSIs. To tackle these issues, we propose SANet, a network designed to enhance crop classification. SANet integrates spectral and contextual information while emphasizing self-correlation within the HSIs. It combines the spatial–spectral nonlocal block structure and the multiscale spectral self-attention (SSA) structure, allocating more attention resources to spatial and spectral dimensions and modeling the existing correlations within the spectral–spatial domain. Additionally, we introduce a two-branch spatial–spectral semantic extraction and fusion structure that can adaptively learn results from both branches. Experimental results demonstrate the promising performance of SANet in agricultural HSI classification by effectively utilizing spectral data, contextual information, and self-attention mechanisms.