  Convolutional neural networks (CNNs) have been widely used for hyperspectral
image classification. As a common process, small cubes are firstly cropped from
the hyperspectral image and then fed into CNNs to extract spectral and spatial
features. It is well known that different spectral bands and spatial positions
in the cubes have different discriminative abilities. If fully explored, this
prior information will help improve the learning capacity of CNNs. Along this
direction, we propose an attention aided CNN model for spectral-spatial
classification of hyperspectral images. Specifically, a spectral attention
sub-network and a spatial attention sub-network are proposed for spectral and
spatial classification, respectively. Both of them are based on the traditional
CNN model, and incorporate attention modules to aid networks focus on more
discriminative channels or positions. In the final classification phase, the
spectral classification result and the spatial classification result are
combined together via an adaptively weighted summation method. To evaluate the
effectiveness of the proposed model, we conduct experiments on three standard
hyperspectral datasets. The experimental results show that the proposed model
can achieve superior performance compared to several state-of-the-art
CNN-related models.
