ACWCD: Utilizing Inherent Transformers Information and Prior Knowledge for Weakly Supervised Change Detection
文献类型：期刊论文
| 作者 | Liu, Wenhao2,3; Yu, Zhuoyuan2,3; Luo, Bin1,3 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:4402614 |
| 关键词 | Cams Transformers Feature extraction Neural networks Training Data mining Remote sensing Refining Deep learning Decoding Change detection (CD) class activation maps (CAMs) deep learning high-resolution images multihead self-attention (MHSA) prior knowledge transformers weakly supervised learning |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3527009 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Change detection (CD) using deep learning is crucial for analyzing changes on the Earth's surface. Yet, obtaining accurate, extensive pixel-level labels is difficult and time-consuming. Consequently, there is growing interest in weakly supervised CD (WSCD) using image-level labels, praised for its high efficiency in label acquisition. Nonetheless, the lack of adequate supervision leads many existing WSCD methods to adopt intricate processes, neglecting the inherent information present in the networks. To overcome these challenges, we propose ACWCD, an end-to-end encoder-decoder framework based on transformers for WSCD using image-level labels. The proposed framework is primarily designed for vision transformer (ViT)-related backbones. It generates effective pseudo labels by tapping into the localizing prowess of class activation maps (CAMs) and simultaneously utilizes these labels for pixel-level supervision during training. Specifically, ACWCD comprises two pivotal components: the attention refinement (AR) module and the change priori (CP) constraint. By harnessing the inherent multihead self-attention (MHSA) of transformers, the AR module refines CAMs by producing change attention from MHSA, thereby refining the pseudo labels. Furthermore, utilizing prior knowledge, the CP constraint prevents the AR module from processing samples with unchanged image-level labels, thus addressing the issue of AR generating spurious change areas. In addition, an exclusive threshold is assigned to each pair of images to help differentiate pseudo labels. It also imposes penalties based on the proportion of mispredictions using the designed plug-and-play loss function. To validate the performance of ACWCD, experiments are conducted on three high-resolution remote sensing datasets. The outcomes reveal that the proposed framework not only achieves new state-of-the-art (SOTA) performance within the WSCD domain but also exhibits substantial scalability, as it does not involve any complex processes, serving as a useful baseline for future research. The code is available at https://github.com/WenhaoLiu03/ACWCD. |
| URL标识 | 查看原文 |
| WOS关键词 | NETWORK ; MODEL |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001414425700003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212407]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yu, Zhuoyuan |
| 作者单位 | 1.Beijing Starearth Technol Co Ltd, Beijing 100083, Peoples R China 2.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Wenhao,Yu, Zhuoyuan,Luo, Bin. ACWCD: Utilizing Inherent Transformers Information and Prior Knowledge for Weakly Supervised Change Detection[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:4402614. |
| APA | Liu, Wenhao,Yu, Zhuoyuan,&Luo, Bin.(2025).ACWCD: Utilizing Inherent Transformers Information and Prior Knowledge for Weakly Supervised Change Detection.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,4402614. |
| MLA | Liu, Wenhao,et al."ACWCD: Utilizing Inherent Transformers Information and Prior Knowledge for Weakly Supervised Change Detection".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):4402614. |
入库方式： OAI收割
来源：地理科学与资源研究所
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