Graph inductive learning method for small sample classification of hyperspectral remote sensing images

Abstract

In recent years, deep learning has drawn increasing attention in the field of hyperspectral remote sensing image classification and has achieved great success. However, the traditional convolutional neural network model has a huge parameter space, in order to obtain a better classification model, a large number of labeled samples are often required. In this paper, a graph induction learning method is proposed to solve the problem of small sample in hyperspectral image classification. It treats each pixel of the hyperspectral image as a graph node and learns the aggregation function of adjacent vertices through graph sampling and graph aggregation operations to generate the embedding vector of the target vertex. Experimental results on three well-known hyperspectral data sets show that this method is superior to the current semi-supervised methods and advanced deep learning methods.

Keywords

• hyperspectral image classification
• graph inductive learning
• graph sampling
• graph aggregation
• small sample