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Graph neural network for hyperspectral image clustering

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Graph neural network for hyperspectral image clusteringby Yao Ding ... [et al.].
其他作者:	Ding, Yao.
出版者:	Singapore :Springer Nature Singapore :2025.
面頁冊數:	xii, 157 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Image processing.
電子資源:	https://doi.org/10.1007/978-981-96-7710-8
ISBN:	9789819677108$q(electronic bk.)

Graph neural network for hyperspectral image clustering
Graph neural network for hyperspectral image clustering[electronic resource] /by Yao Ding ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xii, 157 p. :ill., digital ;24 cm. - Intelligent perception and information processing,3059-3816. - Intelligent perception and information processing..

Introduction -- Self-supervised Efficient Low-pass Contrastive Graph Clustering for Hyperspectral Images -- Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering -- Adaptive Homophily Clustering: A Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image -- Pixel-superpixel Contrastive Learning And Pseudo-label correction For Hyperspectral Image Clustering -- Contrastive Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks.

This book investigates detailed hyperspectral image clustering using graph neural network (graph learning) methods, focusing on the overall construction of the model, design of self-supervised methods, image pre-processing, and feature extraction of graph information. Multiple graph neural network-based clustering methods for hyperspectral images are proposed, effectively improving the clustering accuracy of hyperspectral images and taking an important step towards the practical application of hyperspectral images. This book is innovative in content and emphasizes the integration of theory with practice, which can be used as a reference book for graduate students, senior undergraduate students, researchers, and engineering technicians in related majors such as electronic information engineering, computer application technology, automation, instrument science and technology, remote sensing.

ISBN: 9789819677108$q(electronic bk.)

Standard No.: 10.1007/978-981-96-7710-8doiSubjects--Topical Terms:

182627
Image processing.

LC Class. No.: TK5103.2

Dewey Class. No.: 621.382

Graph neural network for hyperspectral image clustering
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505 0 $a Introduction -- Self-supervised Efficient Low-pass Contrastive Graph Clustering for Hyperspectral Images -- Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering -- Adaptive Homophily Clustering: A Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image -- Pixel-superpixel Contrastive Learning And Pseudo-label correction For Hyperspectral Image Clustering -- Contrastive Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks.
520 $a This book investigates detailed hyperspectral image clustering using graph neural network (graph learning) methods, focusing on the overall construction of the model, design of self-supervised methods, image pre-processing, and feature extraction of graph information. Multiple graph neural network-based clustering methods for hyperspectral images are proposed, effectively improving the clustering accuracy of hyperspectral images and taking an important step towards the practical application of hyperspectral images. This book is innovative in content and emphasizes the integration of theory with practice, which can be used as a reference book for graduate students, senior undergraduate students, researchers, and engineering technicians in related majors such as electronic information engineering, computer application technology, automation, instrument science and technology, remote sensing.
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