Fine-Grained Style Alignment and Class Balance for Unsupervised Domain Adaptation in Remote Sensing Image Segmentation

Abstract

Unsupervised domain adaptation (UDA) is an effective method for addressing the domain shift issue between the source and target domains in high-resolution remote sensing images classification. However, existing methods still face significant challenges when dealing with substantial style discrepancies and class imbalance. For instance, the widely used adaptive instance normalization (AdaIN) method reduces the distribution difference by transforming the style of source domain images to match that of the target domain. However, when confronted with large style variations, this method often leads to the loss of fine-grained details such as color, texture, and contrast, negatively impacting the transfer performance of the model. In addition, class imbalance causes the model to be biased toward dominant classes, resulting in decreased overall classification accuracy. To address these issues, this article proposes two innovative modules. First, we introduce the SelfAdaIN module, which precisely controls the style transformation process through an adaptive convolutional generation mechanism. This module effectively prevents the loss of fine details found in traditional AdaIN methods, achieving fine-grained style alignment between the source and target domains. Second, we propose a class balance loss function that adjusts the weight distribution of target domain samples to mitigate the influence of dominant classes and enhance the focus on rare categories, thereby alleviating the issue of class imbalance. Extensive experiments on four cross-domain remote sensing datasets demonstrate that the proposed methods significantly improve classification accuracy.