Hyperspectral Image Classification Based on Atrous Convolution Channel Attention-Aided Dense Convolutional Neural Network

Hyperspectral image (HSI) classification is a vital but difficult task due to its significant spectral variability and nonlinear structure. Nowadays, complex spatial-spectral networks have achieved remarkable successes in HSI classification, but limited by the large complexity and hardware demands. Spectral networks with simple architectures alleviate this problem to some degree; however, most of them have downgraded performance as a result of insufficient excavation of spectral diagonal information and channel correlations. To overcome these problems, this article proposes a fresh atrous convolution channel attention-aided dense convolutional neural network (ACADCN) for HSI classification, which enhances the exploitation of spectral feature representations and channel correlations to provide a better classification with limited samples. On the one hand, an effective 1-D dense block is constructed to deeply mine spectral discriminability by taking the advantages of hierarchical representations and establish a deep 1-D convolutional neural network (1D CNN), with the complementarity of different level features integrated. On the other hand, a singularly designed atrous convolution channel attention (ACA) module is used to learn multiscale cross-channel correlations to make up the locality of convolutions. The effectiveness of ACADCN is verified on two commonly used HSIs, with a mean overall accuracy (OA) of 94.09%, an average accuracy (AA) of 94.63%, and a kappa of 0.9254 achieved. The experimental results show its superiority to the other advanced deep spectral classifiers.