Graph Neural Network for Hyperspectral Image Clustering
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Beschreibung
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.
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.
Explores the design mechanism of efficient graph neural network based hyperspectral image clustering methods Applies mathematical and artificial intelligence methods to analyze and innovate graph neural network methods Takes a solid step towards the practical application of hyperspectral image
Autor*in
Yao Ding
Themen in »Graph Neural Network for Hyperspectral Image Clustering«
Self-supervised Deep learning Hyperspectral image clustering graph neural network Graph Learning Low-pass graph denoising Contractive learning Deep clustering Low-pass graph convolution Layer-wise graph attention Self-training neural network Adaptive filter graph encoder Homophily-enhanced structure learning Joint network optimization