Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images
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Beschreibung
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.
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.
Studies main problems of graph neural network classifiers for hyperspectral image classification Provides a design method for hyperspectral image classification based on graph neural network Paves the way for future multimodal data fusion and 3D remote sensing image processing
Autor*in
Yao Ding
Themen in »Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images«
Hyperspectral Remote Sensing Image Graph Neural Network Spectral-spatial feature Semi-supervised Methods unsupervised methods Hyperspectral Image Classification Method Feature Presentation Spectral-spatial extraction