In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e.g., optical or radar). Even when considering only optical images, this task has proven to be challenging as soon as the sensors differ by their spatial and/or spectral resolutions. This paper proposes a novel unsupervised change detection method dedicated to images acquired by such so-called heterogeneous optical sensors. It capitalizes on recent advances which formulate the change detection task into a robust fusion framework. Adopting this formulation, the work reported in this paper shows that any off-the-shelf network trained beforehand to fuse optical images of different spatial and/or spectral resolutions can be easily complemented with a network of the same architecture and embedded into an adversarial framework to perform change detection. A comparison with state-of-the-art change detection methods demonstrates the versatility and the effectiveness of the proposed approach.

中文翻译：

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CD-GAN：一种基于融合的鲁棒生成对抗网络，用于使用异构传感器进行无监督遥感变化检测

在地球观测的背景下，变化检测归结为比较由可能不同空间和/或光谱分辨率或不同模态（例如光学或雷达）的传感器在不同时间获取的图像。即使仅考虑光学图像，一旦传感器的空间和/或光谱分辨率不同，这项任务就被证明是具有挑战性的。本文提出了一种新颖的无监督变化检测方法，专用于这种所谓的异构光学传感器获取的图像。它利用了最新的进展，将变化检测任务制定为一个强大的融合框架。采用这种表述，本文报告的工作表明，任何预先训练以融合不同空间和/或光谱分辨率的光学图像的现成网络都可以轻松地用相同架构的网络进行补充，并嵌入到对抗框架中执行变化检测。与最先进的变化检测方法的比较证明了所提出方法的多功能性和有效性。