GRLDASeg: Unsupervised Domain Adaptive Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery Based on General Representation Learning

Unsupervised domain adaptation semantic segmentation for high spatial resolution remote sensing imagery aims to utilize a large amount of labeled data from the source domain to train a supervised model. This model is then adapted for pixel-dense prediction in the unlabeled target domain. Current methods in remote sensing unsupervised domain adaptation mainly focus on mitigating pseudo-label noise. However, they overlook the commonalities between pixel representations across domains. This oversight results in scattered semantic feature representations, thereby limiting segmentation accuracy. To address this issue, this study proposes a novel method prioritizes the extraction of learning universal features, domain-invariant representations spanning low-dimensional discriminative attributes and globally consistent spatial patterns. Specifically, a low-dimensional feature enhancement module is designed to increase feature diversity in shallow layers by employing two learnable scaling and shifting parameters. Additionally, a high-dimensional feature smoothing module uses Gaussian filtering to balance the distribution of global spatial features, enhancing feature learning in deep layers. Comprehensive evaluations on two remote sensing domain adaptation semantic segmentation datasets demonstrate that the superiority of our method, achieving state-of-the-art performance against contemporary baselines in terms of segmentation accuracy and feature alignment.