Unsupervised domain adaptation for the semantic segmentation of remote sensing images via a class-aware Fourier transform and a fine-grained discriminator

Official URL: https://dx.doi.org/10.1016/j.dsp.2024.104551

The semantic segmentation of remote sensing images is vital for Earth observation purposes. However, its performance can decline significantly due to differences in dataset distributions between training (source) and deployment (target) settings. Unsupervised domain adaptation can be used to counter this problem by leveraging the knowledge acquired from the labelled source domain and by adapting it to the unlabelled target domain. Existing methods focus on either input-level or feature-level alignments, which can be sub-optimal for addressing large domain gaps. To this end, this paper introduces a new unsupervised domain adaptation method that employs concurrently two levels of alignment: first, at the input level, an adaptive Fourier-based image-to-image translation approach is utilised to generate target-styled source images with class-based low-amplitude changes. Then, at the feature level, an adaptive fine-grained domain discriminator is introduced that incorporates class information into two parallel discriminators, for source vs. target and target-styled source image vs. target settings. Experimental results indicate that the proposed method improves significantly cross-domain semantic segmentation performance with respect to the state-of-the-art.