Position-Aware Graph-CNN Fusion Network: An Integrated Approach Combining Geospatial Information and Graph Attention Network for Multiclass Change Detection-Reference-Cited by-同舟云学术

Position-Aware Graph-CNN Fusion Network: An Integrated Approach Combining Geospatial Information and Graph Attention Network for Multiclass Change Detection

Published:2024 Issue: Volume:62 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wang Moyang¹^ORCID,Li Xiang²^ORCID,Tan Kun¹^ORCID,Mango Joseph³^ORCID,Pan Chen⁴,Zhang Di¹^ORCID

Affiliation:

1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China

2. Key Laboratory of Geographic Information Science (Ministry of Education), the Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, and the Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai, China

3. Department of Transportation and Geotechnical Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania

4. Shanghai Municipal Institute of Surveying and Mapping, Shanghai, China

Funder

International Research Center of Big Data for Sustainable Development Goals

Fundamental Research Funds for the Central Universities, Chongqing Natural Science Foundation

Ministry of Education of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10381789.pdf?arnumber=10381789

Reference50 articles.

1. GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

2. Multitask learning for large-scale semantic change detection

3. Asymmetric Siamese Networks for Semantic Change Detection in Aerial Images

4. Image change detection algorithms: a systematic survey

5. Digital change detection in forest ecosystems with remote sensing imagery

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