Building Damage Assessment From Post-Hurricane Imageries Using Unsupervised Domain Adaptation With Enhanced Feature Discrimination

Abstract

Rapid damage assessment after hurricane disasters is crucial for initiating effective emergency response actions. Unsupervised domain adaptation (UDA), which improves the classification accuracy of the target domain with leveraging abundant labeled data of the source domain, exhibits the potential to solve the issue of lacking labeled data in the task of damage building assessment. However, the application of UDA to building damage detection remains a challenge due to the complexity of post-hurricane imageries. The image characteristics of the source domain differ from those of the target domain, and the similarity of interclass samples is high, thereby degrading the transfer performance of UDA. We propose Duplex Alignment Networks with enhanced feature discrimination, which consists of a pair of generative adversarial networks (GANs) and a dedicated classifier, to address this issue. Our approach can be highlighted in two aspects: 1) a pair of GANs are used to eliminate the discrepancy between the source and the target domains by the alignments at the feature and pixel levels and 2) a dedicated classifier is integrated into the second discriminator to improve the discrimination of the extracted features of the target domain, which can alleviate performance deterioration caused by the high similarity of interclass samples. The proposed approach is validated by two challenging transfer tasks by using the data sets of Hurricanes Sandy, Maria, and Irma. Average accuracy rates of 85.1% (Hurricane Sandy $\to $ Maria) and 98.3% (Hurricane Sandy $\to $ Irma) are achieved, thereby leading to superior performance compared with the state-of-the-art methods.