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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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Detalles Bibliográficos
Main Authors: Ding, Yao (Author), Zhang, Zhili (Author), Hu, Haojie (Author), He, Fang (Author), Cheng, Shuli (Author), Zhang, Yijun (Author)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:inglés
Publicado: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edición: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.
Acceso en liña:https://doi.org/10.1007/978-981-97-8009-9
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Descripción
Summary: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.
Descrición Física:1 online resource (XII, 183 p. 73 illus.). 67 illus. in color.
ISBN:9789819780099
ISSN:3059-3816
DOI:10.1007/978-981-97-8009-9

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