Joint content-aware and difference-transform lightweight network for remote sensing images semantic change detection
发布时间：2025-11-01
点击次数：
- DOI码：
- 10.1016/j.inffus.2025.103276
- 所属单位：
- Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China
- 发表刊物：
- INFORMATION FUSION
- 项目来源：
- This work is supported by National Key Research and Development Program of China with grant number 2
- 关键字：
- Semantic change detection,Semantic segmentation,Binary change detection,Lightweight network,Remote sensing images
- 摘要：
- Advancements in Earth observation technology have enabled effective monitoring of complex surface changes. Semantic change detection (SCD) using high-resolution remote sensing images is crucial for urban planning and environmental monitoring. However, existing deep learning-based SCD methods, which combine semantic segmentation (SS) and binary change detection (BCD), face challenges in lightweight design and consistency between semantic and change results, limiting their accuracy and applicability. To overcome these limitations, we propose the Joint Content-Aware and Difference-Transform Light
- 合写作者：
- Zhang, Ruiqian,Huang, Xiao,Zhang, Zhizheng,Cai, Bowen,Lv, Xianwei,Li, Deren
- 第一作者：
- Zhang, Jindou
- 论文类型：
- 期刊论文
- 通讯作者：
- Shao, Zhenfeng
- 文献类型：
- Article
- 卷号：
- 123
- ISSN号：
- 1566-2535
- 是否译文：
- 否
- 发表时间：
- 2025-11-01
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