Change detection in satellite imagery seeks to find occurrences of targeted changes in a given scene taken at different instants. This task has several applications ranging from land-cover mapping, to anthropogenic activity monitory as well as climate change and natural hazard damage assessment. However, change detection is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we devise a novel algorithm for change detection based on active learning. The proposed method is based on a question and answer model that probes an oracle (user) about the relevance of changes only on a small set of critical images (referred to as virtual exemplars), and according to oracle's responses updates deep neural network (DNN) classifiers. The main contribution resides in a novel adversarial model that allows learning the most representative, diverse and uncertain virtual exemplars (as inverted preimages of the trained DNNs) that challenge (the most) the trained DNNs, and this leads to a better re-estimate of these networks in the subsequent iterations of active learning. Experiments show the out-performance of our proposed deep-net inversion against the related work.

翻译：卫星图像变化检测旨在发现同一场景在不同时刻获取的图像中发生的目标变化。该任务具有多种应用，包括土地覆盖制图、人类活动监测以及气候变化和自然灾害损害评估。然而，由于采集条件和变化的主观性，变化检测极具挑战性。本文提出了一种基于主动学习的新型变化检测算法。该方法基于问答模型，仅对少量关键图像（称为虚拟样本）向用户（ oracle ）询问变化的相关性，并根据用户反馈更新深度神经网络分类器。主要贡献在于一种新颖的对抗模型，该模型能够学习最具代表性、多样性且不确定性最高的虚拟样本（作为训练深度神经网络的逆像），从而最大程度地挑战训练好的深度神经网络，并在后续主动学习迭代中实现对这些网络的更优重估计。实验表明，我们提出的深度网络反演方法在性能上优于现有相关工作。