Semisupervised graph convolutional network for hyperspectral image classification
Bing Liu, Kuiliang Gao, Anzhu Yu, Wenyue Guo, Ruirui Wang, Xibing Zuo
Abstract
Deep learning has been widely used in hyperspectral image (HSI) classification. However, a deep learning model is a data-driven machine learning method, and collecting labeled data is quite time-consuming for an HSI classification task, which means that a deep learning model needs a lot of labeled data and cannot deal with the small sample problem. We explore the small sample classification problem of HSI with graph convolutional network (GCN). First, HSI with a small number of labeled samples are treated as a graph. Then, the GCN (an efficient variant of convolutional neural networks) operates directly on the graph constructed from the HSI. GCN utilizes the adjacency nodes in graph to approximate the convolution. In other words, graph convolution can use both labeled and unlabeled nodes. Therefore, our method is a semisupervised method. Three HSI are used to assess the performance of the proposed method. The experimental results show that the proposed method outperforms the traditional semisupervised methods and advanced deep learning methods.