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
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Shrnutí: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 categories are very typical, making it challenging to accurately distinguish the easily confused categories with limited semantic context information. However, previous works did not address these issues in depth. This article proposes a novel SCD method named semi-supervised contrastive learning (SSCLNet), in which a simple and effective SCD network is designed as a strong baseline, and a semi-supervised contrastive learning module of semantic segmentation (SS) is presented to enhance the distinguishability of categories. Our baseline extracts semantic context through high-resolution network (HRNet), gets change information simply through an absolute difference, and then directly performs SCD based on the fusion of semantic context and change information. To utilize the semantic context information of the unlabeled non-changed regions, we employ a self-training (ST) method for semi-supervised SS. To learn distinguishable feature representations for easily confused categories, we present contrastive learning with an adaptive sampling strategy for SS. It selects challenging negative samples for each category from the other categories that exhibit similar features or attributes. The sampling space includes both the labeled changed samples and the non-changed samples predicted by ST. The comprehensive experiments on the SECOND and the Landsat-SCD dataset demonstrate that the proposed SSCLNet achieves the state-of-the-art (SOTA) performance, with a significant improvement of 2.07% and 4.15% in the score value, respectively.
Bibliografie:ObjectType-Article-1
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3395135