Frequency-Based Optimal Style Mix for Domain Generalization in Semantic Segmentation of Remote Sensing Images

Supervised learning methods assume that training and test data are sampled from the same distribution. However, this assumption is not always satisfied in practical situations of land cover semantic segmentation when models trained in a particular source domain are applied to other regions. This is because domain shifts caused by variations in location, time, and sensor alter the distribution of images in the target domain from that of the source domain, resulting in significant degradation of model performance. To mitigate this limitation, domain generalization (DG) has gained attention as a way of generalizing from source domain features to unseen target domains. One approach is style randomization (SR), which enables models to learn domain-invariant features through randomizing styles of images in the source domain. Despite its potential, existing methods face several challenges, such as inflexible frequency decomposition, high computational and data preparation demands, slow speed of randomization, and lack of consistency in learning. To address these limitations, we propose a frequency-based optimal style mix (FOSMix), which consists of three components: 1) full mix (FM) enhances the data space by maximally mixing the style of reference images into the source domain; 2) optimal mix (OM) keeps the essential frequencies for segmentation and randomizes others to promote generalization; and 3) regularization of consistency ensures that the model can stably learn different images with the same semantics. Extensive experiments that require the model's generalization ability, with domain shift caused by variations in regions and resolutions, demonstrate that the proposed method achieves superior segmentation in remote sensing. The source code is available at https://github.com/Reo-I/FOSMix.