Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented & real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of
翻译:变化探测 (CD) 是指将目标变化(即对象缺失或出现)与背景变化(即环境变异)区分开来,如在长时间内在同一场景中捕捉到的两张图像中的光和季节变化,展示灾害管理、城市发展等的关键应用。 特别是,无休止的背景变化模式要求探测器对看不见的环境变化有高度的概括化,使这项任务具有极大的挑战性。最近深层次的基于学习的方法开发出新的网络结构或优化战略,并配对培训范例,这些范例并不明确处理一般化问题,需要大量的手工像素级说明。在这项工作中,为了在CD界的第一次尝试,我们从数据增强的角度研究光盘的通用问题,并开发出一种新的、监管薄弱的培训算法,仅需要图像级标签。不同于一般增强技术,我们提出了专门设计用于变化探测的背景组合增强能力,在一套背景变化图像变化和深层次的CD模型的指导下,增加示例环境变化。 此外,我们建议扩大深度和真实数据一致性的大规模实验方法,我们可以大大加强现有的四种实验方法。