Scene Changes Understanding Framework Based on Graph Convolutional Networks and Swin Transformer Blocks for Monitoring LCLU Using High-Resolution Remote Sensing Images

High-resolution remote sensing images with rich land surface structure can provide data support for accurately understanding more detailed change information of land cover and land use (LCLU) at different times. In this study, we present a novel scene change understanding framework for remote sensin...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 14; H. 15; S. 3709
Hauptverfasser:	Yang, Sihan, Song, Fei, Jeon, Gwanggil, Sun, Rui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.08.2022
Schlagworte:	Algorithms Artificial neural networks Change detection Classification data collection Deep learning High resolution high-resolution remote sensing images Image classification Image enhancement Image resolution label semantic relation Land cover Land use land use change LCLU Learning Monitoring Neural networks Remote sensing Remote sensing systems scene change understanding Semantic relations Semantics Surface structure transformer Transformers Siam
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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