用生成式虚拟样本减少标注需求，提升遥感图像变化检测效率
Label-frugal satellite image change detection with generative virtual exemplar learning
- 基于主动学习构建虚拟样本筛选机制，仅选取最关键未标注数据
- 通过对抗损失优化生成样本，兼顾代表性、多样性和模糊性
- 显著降低标注依赖，适合标注成本高的遥感变化检测任务
变化检测是遥感领域的重要任务，旨在从多时相卫星或航拍图像中识别所有变化区域。现有方法尤其是深度学习方法的成功高度依赖于人工标注的训练数据，这些数据需涵盖成像条件和用户主观判断（即‘专家’）。本文提出一种新型变化检测算法，基于主动学习框架。核心贡献在于设计了一种新模型，可衡量每个未标注样本的重要性，并仅向专家提供最关键的样本（称为虚拟示例）进行标注。这些虚拟示例通过可逆图卷积网络生成，作为对抗损失的最优解，该损失同时衡量数据的代表性、多样性和模糊性，从而最大程度挑战当前变化检测判据，在后续主动学习迭代中实现更优判据重估。大量实验表明，所提标签高效学习模型在性能上优于对比方法。
原文摘要 · Abstract (English)
Change detection is a major task in remote sensing which consists in finding all the occurrences of changes in multi-temporal satellite or aerial images. The success of existing methods, and particularly deep learning ones, is tributary to the availability of hand-labeled training data that capture the acquisition conditions and the subjectivity of the user (oracle). In this paper, we devise a novel change detection algorithm, based on active learning. The main contribution of our work resides in a new model that measures how important is each unlabeled sample, and provides an oracle with only the most critical samples (also referred to as virtual exemplars) for further labeling. These exemplars are generated, using an invertible graph convnet, as the optimum of an adversarial loss that (i) measures representativity, diversity and ambiguity of the data, and thereby (ii) challenges (the most) the current change detection criteria, leading to a better re-estimate of these criteria in the subsequent iterations of active learning. Extensive experiments show the positive impact of our label-efficient learning model against comparative methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。