Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting

• URL: http://arxiv.org/abs/2508.17186v1
• Date: Sun, 24 Aug 2025 02:02:16 GMT
• Title: Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting
• Authors: Zhenghui Zhao, Chen Wu, Di Wang, Hongruixuan Chen, Cuiqun Chen, Zhuo Zheng, Bo Du, Liangpei Zhang,
• Abstract summary: We propose an Adversarial Class Prompting (AdvCP) method to address this co-occurring noise problem.<n>Our AdvCP can be seamlessly integrated into current WSCD methods without adding additional inference cost.
• Score: 49.15470825004932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly-Supervised Change Detection (WSCD) aims to distinguish specific object changes (e.g., objects appearing or disappearing) from background variations (e.g., environmental changes due to light, weather, or seasonal shifts) in paired satellite images, relying only on paired image (i.e., image-level) classification labels. This technique significantly reduces the need for dense annotations required in fully-supervised change detection. However, as image-level supervision only indicates whether objects have changed in a scene, WSCD methods often misclassify background variations as object changes, especially in complex remote-sensing scenarios. In this work, we propose an Adversarial Class Prompting (AdvCP) method to address this co-occurring noise problem, including two phases: a) Adversarial Prompt Mining: After each training iteration, we introduce adversarial prompting perturbations, using incorrect one-hot image-level labels to activate erroneous feature mappings. This process reveals co-occurring adversarial samples under weak supervision, namely background variation features that are likely to be misclassified as object changes. b) Adversarial Sample Rectification: We integrate these adversarially prompt-activated pixel samples into training by constructing an online global prototype. This prototype is built from an exponentially weighted moving average of the current batch and all historical training data. Our AdvCP can be seamlessly integrated into current WSCD methods without adding additional inference cost. Experiments on ConvNet, Transformer, and Segment Anything Model (SAM)-based baselines demonstrate significant performance enhancements. Furthermore, we demonstrate the generalizability of AdvCP to other multi-class weakly-supervised dense prediction scenarios. Code is available at https://github.com/zhenghuizhao/AdvCP

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