GDPA_LDSICS: graph and double pyramid attention network based on linear discrimination of spectral interclass slices for hyperspectral image classification

Abstract

ABSTRACT In recent years, convolution neural networks (CNNs) and graph convolution networks (GCNs) have been widely used in hyperspectral image classification (HSIC). CNNs can effectively extract the spatial spectral features of hyperspectral images (HSIs), while GCNs can quickly capture the structural features of HSIs, which makes the effective combination of the two is beneficial to improve classification performance of hyperspectral images. However, the high redundancy of feature information and the problem of small sample are still the major challenges of HSIC. In order to alleviate these problems, in this paper, a new graph and double pyramid attention network based on linear discrimination of spectral interclass slices (GDPA_LDSICS) is proposed. First, a linear discrimination of spectral inter class slices (LDSICS) module is designed. The LDSICS module can effectively eliminate a lot of redundancy in spectral dimension, which is conducive to subsequent feature extraction. Then, the spatial spectral deformation (SSD) module is constructed, which can effectively correlate the spatial spectral information closely. Finally, in order to alleviate the problem of small sample, a double branch structure of CNN and GCN is developed. On the CNN branch, a double pyramid attention (DPA) structure is designed to model context semantics to avoid information loss caused by long-distance feature extraction. On the GCN branch, an adaptive dynamic encoding (ADE) method is proposed, which can more effectively capture the topological structure of spatial spectral features. Experiments on four open datasets show that the GDPA_LDSICS can provide better classification performance and generalization performance than other most advanced methods.