Semi-Supervised Classification for Hyperspectral Images Using Edge-Conditioned Graph Convolutional Networks

Abstract

The imbalance between high dimensionality and limited labeled samples has been a great challenge for classification task of hyperspectral images (HSIs). In this paper, a novel semi-supervised classification method for HSIs is proposed. This method contains two major parts: representation using spatial-spectral graph model and graph convolutional networks (GCN) using edge-conditioned convolution. For the proposed method, spatial-spectral information is considered simultaneously during the process of graph construction, and then GCN is used to extract the feature from input data and learn their topology relationships with edge label involved. Experimental results on multiple hyperspectral datasets with various contexts and resolutions demonstrate that the proposed classifier outperforms several graph-based methods.