A Brief Review of Graph Convolutional Neural Network Based Learning for classifying remote sensing images

Abstract

Researchers have recently begun investigating graph Convolutional neural networks for analyzing remotely sensed images. Modelling an image as a graph is a great way to understand its semantic, and applying the convolution on the graph structure have shown promising results in many real-world application. The hope is that such models help to improve the quality of land cover maps produced by the interpretation of satellite imagery. In this paper, we review recently proposed models based on graph convolution neural network architectures and discuss the challenge that could be taken up to improve the results of this technique and how can we use it for transfer learning between model.