In the context of Earth observation, the detection of changes is performed from multitemporal images acquired by sensors with possibly different spatial and/or spectral resolutions or even different modalities (e.g. optical, radar). Even limiting to the optical modality, this task has proved to be challenging as soon as the sensors have different spatial and/or spectral resolutions. This paper proposes a novel unsupervised change detection method dedicated to images acquired with such so-called heterogeneous optical sensors. This method capitalizes on recent advances which frame the change detection problem into a robust fusion framework. More precisely, we show that a deep adversarial network designed and trained beforehand to fuse a pair of multiband optical images can be easily complemented by a network with the same architecture to perform change detection. The resulting overall architecture itself follows an adversarial strategy where the fusion network and the additional network are interpreted as essential building blocks of a generator. A comparison with state-of-the-art change detection methods demonstrates the versatility and the effectiveness of the proposed approach.

翻译：在地球观测背景下，变化检测通常通过多时相图像进行，这些图像由可能具有不同空间和/或光谱分辨率甚至不同模态（如光学、雷达）的传感器获取。即使仅限于光学模态，一旦传感器具有不同的空间和/或光谱分辨率，这一任务已被证明具有挑战性。本文提出了一种专为这种所谓异构光学传感器获取的图像设计的新型无监督变化检测方法。该方法利用了将变化检测问题纳入鲁棒融合框架的最新进展。更具体地说，我们展示了预先设计和训练用于融合一对多波段光学图像的深度对抗网络，可以轻松地通过具有相同架构的网络进行补充，从而实现变化检测。由此产生的整体架构本身遵循对抗策略，其中融合网络和附加网络被解释为生成器的基本构建模块。与最新变化检测方法的比较证明了所提出方法的通用性和有效性。