Hyperspectral Image Classification Based on a 3D Octave Convolution and 3D Multiscale Spatial Attention Network

Convolutional neural networks are widely used in the field of hyperspectral image classification. After continuous exploration and research in recent years, convolutional neural networks have achieved good classification performance in the field of hyperspectral image classification. However, we have to face two main challenges that restrict the improvement of hyperspectral classification accuracy, namely, the high dimension of hyperspectral images and the small number of training samples. In order to solve these problems, in this paper, a new hyperspectral classification method is proposed. First, a three-dimensional octave convolution (3D-OCONV) is proposed. Subsequently, a dense connection structure of three-dimensional asymmetric convolution (DC-TAC) is designed. In the spectral branch, the spectral features are extracted through a combination of the 3D-OCONV and spectral attention modules, followed by the DC-TAC. In the spatial branch, a three-dimensional, multiscale spatial attention module (3D-MSSAM) is presented. The spatial information is fully extracted using the 3D-OCONV, 3D-MSSAM, and DC-TAC. Finally, the spectral and spatial information extracted from the two branches is fully fused with an interactive information fusion module. Compared to some state-of-the-art classification methods, the proposed method shows superior classification performance with a small number of training samples on four public datasets.

Automated summary

Convolutional neural networks have been widely used in hyperspectral image classification and have achieved good performance. However, the high dimension of hyperspectral images and limited training samples pose challenges. To address this, the paper proposes a new method that combines several attention modules to extract spectral and spatial features and achieves superior classification performance with limited training samples.