A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection

Nonagriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the la...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 15; S. 4297 - 4306
Hauptverfasser:	Liu, Mengxi, Chai, Zhuoqun, Deng, Haojun, Liu, Rong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Agglomeration Aggregation Agricultural ecosystems Agricultural land Biological system modeling Change detection Change detection (CD) Computer applications Context cropland Data mining Datasets Decoding Deep layer deep learning (DL) Detection Feature extraction Food security Head High resolution Image resolution Remote sensing Resolution Spatial discrimination Spatial resolution Task analysis transformer Transformers
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
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