Building damage assessment plays an essential role during post-disaster rescue operations. Given that labeled samples are difficult to timely obtain after a disaster, transfer learning attracts increasing attention. However, different sensors employed cause considerable discrepancies not only between historical and current scenes but also among historical scenes, which could exert an effect on transfer performance. Therefore, a multi-source adversarial domain adaptation (MADA) method is proposed in this paper to fulfill the task of post-disaster building assessment. This method consists of two phases. First, imageries of several historical scenes are transformed into the same style of the current scene through the CycleGAN model with a classifier, ensuring class invariance, to be fused to make an adapted source domain. Second, feature alignment between adapted source and target domains is executed based on adversarial discriminative domain adaptation. The MADA method enhances the transformed image quality, fully utilizes relevant information in historical scenes, solves inter-scene interference problems among historical images, and improves the transfer efficiency from historical to the current disaster scene. Two experiments are conducted with Hurricane Sandy, Irma, and Maria datasets as multi-source and target domains to validate MADA’s effectiveness. Results show that the classification performance is better than other methods.

中文翻译：

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基于多源对抗域自适应的建筑损伤检测

建筑物损坏评估在灾后救援行动中起着至关重要的作用。鉴于灾难发生后难以及时获取标记样本，迁移学习引起了越来越多的关注。然而，使用的不同传感器不仅会导致历史和当前场景之间以及历史场景之间存在相当大的差异，这可能会对传输性能产生影响。因此，本文提出了一种多源对抗域适应（MADA）方法来完成灾后建筑评估任务。该方法由两个阶段组成。首先，将几个历史场景的图像通过带有分类器的 CycleGAN 模型转换为当前场景的相同风格，确保类不变性，然后融合以形成适应的源域。第二，自适应源域和目标域之间的特征对齐是基于对抗性判别域自适应执行的。MADA方法提高了转换后的图像质量，充分利用了历史场景中的相关信息，解决了历史图像之间的场景间干扰问题，提高了历史到当前灾害场景的传输效率。使用飓风桑迪、艾尔玛和玛丽亚数据集作为多源域和目标域进行了两个实验，以验证 MADA 的有效性。结果表明分类性能优于其他方法。解决了历史图像之间的场景间干扰问题，提高了历史到当前灾害场景的传输效率。使用飓风桑迪、艾尔玛和玛丽亚数据集作为多源域和目标域进行了两个实验，以验证 MADA 的有效性。结果表明分类性能优于其他方法。解决了历史图像之间的场景间干扰问题，提高了历史到当前灾害场景的传输效率。使用飓风桑迪、艾尔玛和玛丽亚数据集作为多源域和目标域进行了两个实验，以验证 MADA 的有效性。结果表明分类性能优于其他方法。