Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images [electronic resource] / by Yao Ding, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, Yijun Zhang.

Por: Ding, Yao [author.]Colaborador(es): Zhang, Zhili [author.] | Hu, Haojie [author.] | He, Fang [author.] | Cheng, Shuli [author.] | Zhang, Yijun [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Intelligent Perception and Information ProcessingEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XII, 183 p. 73 illus., 67 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819780099Tema(s): Image processing | Neural networks (Computer science)  | Machine learning | Image Processing | Mathematical Models of Cognitive Processes and Neural Networks | Machine LearningFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 621.382 Clasificación LoC:TA1637-1638Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer Nature eBookResumen: 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.
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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.

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