Multiscale semantic alignment graph convolution network for single-shot learning based hyperspectral image classification

Abstract

Graph convolutional networks (GCN) can extract features from non-Euclidean space very effectively, and it has been successfully applied in various fields of hyperspectral images (HSIs). However, due to the limited labeled HSI data, GCN often performs not well and encounters over-smoothing problems as the number of network layers increases. Furthermore, building a GCN adjacency matrix for HSI classification directly is computationally complex. This paper proposes a multiscale semantic alignment graph convolutional network (MSAGCN) for HSI classification to solve the problems mentioned above. The proposed method mainly consists of three parts, superpixel segmentation, semantic alignment and multiscale graphs. Firstly, superpixel segmentation is performed on the original HSI, and each superpixel region contains similar spatial and spectral information. Secondly, semantic features of labeled nodes are extracted using identity aggregation with fixed receptive fields. The class-center similarity is adopted using these semantic features to align nodes semantically. This semantic alignment technique alleviates over-smoothing in GCN. Finally, the multiscale technique enables MSAGCN to obtain different scale spectral-spatial features. Experimental results show that our proposed model exhibits competitive results on open source hyperspectral datasets with only one labeled sample per class.