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Convolutional neural networks (CNNs) have been widely used for hyperspectralimage classification. As a common process, small cubes are firstly cropped fromthe hyperspectral image and then fed into CNNs to extract spectral and spatialfeatures. It is well known that different spectral bands and spatial positionsin the cubes have different discriminative abilities. If fully explored, thisprior information will help improve the learning capacity of CNNs. Along thisdirection, we propose an attention aided CNN model for spectral-spatialclassification of hyperspectral images. Specifically, a spectral attentionsub-network and a spatial attention sub-network are proposed for spectral andspatial classification, respectively. Both of them are based on the traditionalCNN model, and incorporate attention modules to aid networks focus on morediscriminative channels or positions. In the final classification phase, thespectral classification result and the spatial classification result arecombined together via an adaptively weighted summation method. To evaluate theeffectiveness of the proposed model, we conduct experiments on three standardhyperspectral datasets. The experimental results show that the proposed modelcan achieve superior performance compared to several state-of-the-artCNN-related models.

Hyperspectral Image Classification with Attention Aided CNNs