Hyperspectral Image Classification Based on Dual-Branch Spectral Multiscale Attention Network

Abstract

In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image classification and have achieved good performance. However, the high dimensions and few samples of hyperspectral remote sensing images tend to be the main factors restricting improvements in classification performance. At present, most advanced classification methods are based on the joint extraction of spatial and spectral features. In this article, an improved dense block based on a multiscale spectral pyramid (MSSP) is proposed. This method uses the idea of multiscale and group convolution of the convolution kernel, which can fully extract spectral information from hyperspectral images. The designed MSSP is the main unit of the spectral dense block (called MSSP Block). Additionally, a short connection with nonlinear transformation is introduced to enhance the representation ability of the model. To demonstrate the effectiveness of the proposed dual-branch multiscale spectral attention network, some experiments are conducted on five commonly used datasets. The experimental results show that, compared with some state-of-the-art methods, the proposed method can provide better classification performance and has strong generalization ability.