Abstract. Geographic variability of the classes of interest, differences in sensor characteristics and changes in atmospheric conditions during image acquisition, among other factors, make it challenging to use a pre-trained deep learning classifier on new remote sensing data without a substantial drop in classification accuracy. This phenomenon occurs due to the so-called domain shift problem. Deep domain adaptation techniques have been used to mitigate the problem and thus avoid the time-consuming and costly collection of new labeled samples. Most recent domain adaptation approaches rely on single-source and single-target domains, refraining from exploiting other data distributions that are usually available. This work introduces a new unsupervised multi-target domain adaptation in the context of a change detection application, namely deforestation detection. The proposed approach addresses the substantial class imbalance typical of such application by applying unsupervised algorithms for selecting pseudo-labels in the target domain that will later serve as additional training references. We report results of experiments to evaluate the proposed method in four distinct sites of two Brazilian biomes using Sentinel-2 images. The results indicate that the proposed unsupervised domain adaptation method is a promising solution to reduce the effects of domain shift and to deal with the scarcity of labeled training data.