HSGACN: Hyperspectral Image Classification Algorithm Based on Graph Convolutional Network

Abstract

Convolutional neural network has been widely used in hyperspectral image classification. Compared with the early machine learning method, it has made great progress. However, the convolution kernel used in hyperspectral image classification ignores the intrinsic relationship among spatial pixels when extracting spectral features, which will lead to poor contour and very small false prediction in the classification results. Besides, The hyperspectral data can only be labeled by experts, which requires a lot of labor and material resources. In order to improve the classification accuracy of hyperspectral images and reduce the dependence on labeled samples, this paper proposed a hyperspectral image classification algorithm based on graph neural network. Through the characteristics of inherent points and edges in the graph, the spatial information and spectral information of hyperspectral images are fused. The features of unlabeled samples are used to participate in the training to improve the effect of classification model.