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Domain adaptation for semantic segmentation has recently been activelystudied to increase the generalization capabilities of deep learning models.The vast majority of the domain adaptation methods tackle single-source case,where the model trained on a single source domain is adapted to a targetdomain. However, these methods have limited practical real world applications,since usually one has multiple source domains with different datadistributions. In this work, we deal with the multi-source domain adaptationproblem. Our method, namely StandardGAN, standardizes each source and targetdomains so that all the data have similar data distributions. We then use thestandardized source domains to train a classifier and segment the standardizedtarget domain. We conduct extensive experiments on two remote sensing datasets, in which the first one consists of multiple cities from a single country,and the other one contains multiple cities from different countries. Ourexperimental results show that the standardized data generated by StandardGANallow the classifiers to generate significantly better segmentation.

StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization