Joint spatial-spectral attention network for hyperspectral image classification

Hyperspectral images (HSIs) contain rich context information in the spatial domain and spectral domain. To fully explore that information, a data-driven joint spatial-spectral attention network (JSSAN) is proposed in this letter. Specifically, we first design a spatial-spectral attention (S2A) block to simultaneously capture long-range interdependency of spatial and spectral data via the similarity evaluation. Then we adopt a weighted sum operation of features at all spatial positions and channels to selectively aggregate discriminative spatial- spectral features. Second, the S2A block is inserted into simple convolutional neural network (CNN) structure to extract more representative features for classification, by adaptively emphasizing features of informative land covers and spectral bands which contribute more to class identification. The experimental results reveal that our proposed method outperforms several state-ofthe- art algorithms.