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.