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.

翻译：在地球观测背景下，变化检测归结为比较由可能具有不同空间和/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。即便仅考虑光学图像，一旦传感器在空间和/或光谱分辨率上存在差异，该任务也已被证明具有挑战性。本文提出了一种专用于此类所谓异构光学传感器获取图像的新型无监督变化检测方法。该方法借鉴了近期将变化检测任务形式化为鲁棒融合框架的进展。采用这一形式化表述，本文报告的工作表明，任何预先训练用于融合不同空间和/或光谱分辨率光学图像的现成网络，均可轻松地通过相同架构的网络进行补充，并嵌入对抗性框架中，以执行变化检测。与现有最优变化检测方法的比较证明了所提方法的通用性与有效性。