Description
This book deals with hyperspectral image classification using graph neural network methods, focusing on classification model designing, graph information dissemination, and graph construction. In the book, various graph neural network based classifiers have been proposed for hyperspectral image classification to improve the classification accuracy. This book has promoted the application of graph neural network in hyperspectral image classification, providing reference for remote sensing image processing. It will be a useful reference for researchers in remote sensing image processing and image neural network design.
Table of Contents
Introduction.- Graph sample and aggregate-attention network for hyperspectral image classification.- Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification.- Pixel and hyperpixel level feature combining for hyperspectral image classification.- Global dynamic graph optimization for hyperspectral image classification.- Exploring relationship between transformer and graph convolution for hyperspectral image classification.
