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Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images

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 clas...

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Bibliographic Details
Main Authors: Ding, Yao (Author), Zhang, Zhili (Author), Hu, Haojie (Author), He, Fang (Author), Cheng, Shuli (Author), Zhang, Yijun (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edition:1st ed. 2024.
Series:Intelligent Perception and Information Processing,
Subjects:
Image processing.
Neural networks (Computer science) .
Machine learning.
Image Processing.
Mathematical Models of Cognitive Processes and Neural Networks.
Machine Learning.
Online Access:https://doi.org/10.1007/978-981-97-8009-9
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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.

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