Multiorder Graph Convolutional Network With Channel Attention for Hyperspectral Change Detection
Author:
Affiliation:
1. School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China
2. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
3. School of Computer, Wuhan University, Wuhan, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/10330207/10342770.pdf?arnumber=10342770
Reference62 articles.
1. Change detection from remotely sensed images: From pixel-based to object-based approaches
2. Review Article Digital change detection techniques using remotely-sensed data
3. Deep Learning for Hyperspectral Image Classification: An Overview
4. Hyperspectral Image Classification: Potentials, Challenges, and Future Directions
5. A critical synthesis of remotely sensed optical image change detection techniques
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1. Pyramid Cascaded Convolutional Neural Network with Graph Convolution for Hyperspectral Image Classification;Remote Sensing;2024-08-11
2. SAGN: Sharpening-Aware Graph Network for Hyperspectral Image Change Detection;IEEE Transactions on Geoscience and Remote Sensing;2024
3. Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
4. Cross-Task Meta-Learning Network With Graph-Enhanced Attention Module for Hyperspectral Change Detection;IEEE Transactions on Geoscience and Remote Sensing;2024
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