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In the field of remote sensing and more specifically in Earth Observation,new data are available every day, coming from different sensors. Leveraging onthose data in classification tasks comes at the price of intense labellingtasks that are not realistic in operational settings. While domain adaptationcould be useful to counterbalance this problem, most of the usual methodsassume that the data to adapt are comparable (they belong to the same metricspace), which is not the case when multiple sensors are at stake. Heterogeneousdomain adaptation methods are a particular solution to this problem. We presenta novel method to deal with such cases, based on a modified cycleGAN versionthat incorporates classification losses and a metric space alignment term. Wedemonstrate its power on a land use classification tasks, with images from bothGoogle Earth and Sentinel-2.

A Cycle GAN Approach for Heterogeneous Domain Adaptation in Land Use Classification