Remote Sensing Image Semantic Change Detection Boosted by Semi-Supervised Contrastive Learning of Semantic Segmentation

Semantic change detection (SCD) is a challenging task in remote sensing image (RSI) interpretation, which adopts multitemporal images to detect, locate, and analyze pixel-level land-cover "from-to" changes. In SCD, the severe class imbalance problem and the occurrence of confusing categori...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 13
Hlavní autoři:	Zhang, Xiuwei, Yang, Yizhe, Ran, Lingyan, Chen, Liang, Wang, Kangwei, Yu, Lei, Wang, Peng, Zhang, Yanning
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptive sampling Categories Change detection Context Contrastive learning Earth Image segmentation Information processing Land cover Landsat Learning Remote sensing Sampling Self-supervised learning self-training (ST) semantic change detection (SCD) Semantic segmentation Semantics Semi-supervised learning Task analysis Training
ISSN:	0196-2892, 1558-0644
On-line přístup:	Získat plný text
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