TY  - BOOK
AU  - Ding,Yao
AU  - Zhang,Zhili
AU  - Hu,Haojie
AU  - He,Fang
AU  - Cheng,Shuli
AU  - Zhang,Yijun
ED  - SpringerLink (Online service)
TI  - Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images
T2  - Intelligent Perception and Information Processing,
SN  - 9789819780099
AV  - TA1637-1638 
U1  - 621.382 23
PY  - 2024///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Image processing
KW  - Neural networks (Computer science) 
KW  - Machine learning
KW  - Image Processing
KW  - Mathematical Models of Cognitive Processes and Neural Networks
KW  - Machine Learning
N1  - 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-97-8009-9
ER  -