通过对抗提示机制提升卫星图像弱监督变化检测精度
Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting
- 引入对抗提示扰动，挖掘误判的背景变化特征
- 构建动态全局原型，修复错误分类的像素样本
- 可无缝接入现有方法，适合遥感变化检测场景
弱监督变化检测（WSCD）旨在仅依赖成对图像级分类标签的情况下，区分特定目标变化（如物体出现或消失）与背景变化（如光照、天气或季节性变化）。该方法显著降低全监督检测所需的密集标注成本。然而，由于图像级监督仅表明场景中是否存在变化，现有方法常将背景变化误判为对象变化，尤其在复杂遥感场景下。本文提出对抗类别提示（AdvCP）方法，包含两个阶段：a）对抗提示挖掘：每轮训练后引入对抗提示扰动，使用错误的one-hot图像级标签激活错误特征映射，从而揭示在弱监督下易被误判为对象变化的背景变化特征；b）对抗样本修正：将这些对抗提示激活的像素样本整合进训练，通过指数加权移动平均构建在线全局原型。该方法可无缝嵌入当前WSCD模型，不增加推理开销。在基于ConvNet、Transformer和Segment Anything Model（SAM）的基线模型上实验显示性能显著提升。此外，我们验证了AdvCP在其他多类弱监督密集预测任务中的泛化能力。代码已公开于https://github.com/zhenghuizhao/AdvCP。
原文摘要 · Abstract (English)
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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