Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images

Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a targe...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 17; H. 15; S. 2581
Hauptverfasser:	Jia, Meng, Lou, Xiangyu, Zhao, Zhiqiang, Lu, Xiaofeng, Shi, Zhenghao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 24.07.2025
Schlagworte:	Change detection Classification Data structures Datasets Deep learning domain transformation graph attention Heterogeneity heterogeneous remote sensing images Image acquisition Methods Neural networks Performance evaluation Remote sensing Representations Sensors Similarity measures Transformations (mathematics) unsupervised change detection
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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