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Graph neural network for feature extraction and classification of hyperspectral remote sensing images

Record Type:	Electronic resources : Monograph/item
Title/Author:	Graph neural network for feature extraction and classification of hyperspectral remote sensing images/ by Yao Ding ... [et al.].
other author:	Ding, Yao.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xii, 183 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-981-97-8009-9
ISBN:	9789819780099

Graph neural network for feature extraction and classification of hyperspectral remote sensing images
Graph neural network for feature extraction and classification of hyperspectral remote sensing images[electronic resource] /by Yao Ding ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xii, 183 p. :ill., digital ;24 cm. - Intelligent perception and information processing,3059-3816. - Intelligent perception and information processing..

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.

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.

ISBN: 9789819780099

Standard No.: 10.1007/978-981-97-8009-9doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Graph neural network for feature extraction and classification of hyperspectral remote sensing images
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260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xii, 183 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent perception and information processing, $x 3059-3816
505 0 $a 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.
520 $a 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.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Remote sensing $x Data processing. $3 567259
650 1 4 $a Image Processing. $3 891209
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Ding, Yao. $3 3436755
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830 0 $a Intelligent perception and information processing. $3 3734710
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950 $a Engineering (SpringerNature-11647)