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Graph Neural Network for Hyperspectral Image Clustering /

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-b...

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Bibliographic Details
Main Author: Ding, Yao
Other Authors: Zhang, Zhili, Hu, Haojie, Guan, Renxiang, Feng, Jie, Lv, Zhiyong
Format: eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Edition:1st ed. 2025.
Series:Intelligent Perception and Information Processing,
Subjects:
Image processing.
Medicine > Research.
Biology > Research.
Neural networks (Computer science)
Machine learning.
Image Processing.
Biomedical Research.
Mathematical Models of Cognitive Processes and Neural Networks.
Machine Learning.
Traitement d'images.
Médecine > Recherche.
Biologie > Recherche.
Réseaux neuronaux (Informatique)
Apprentissage automatique.
image processing.
Electronic books.
Table of Contents:
  • 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.