Local Pixel-Contrast and Global Gaussian Multiprototype Bidirectional Alignment for Unsupervised Domain Adaptation of Semantic Segmentation in Remote Sensing Imagery

In unsupervised domain adaptation for semantic segmentation of remote sensing imagery, identical land-cover classes across different domains often exhibit substantial variations in appearance, scale, and class distribution, which seriously hinder cross-domain generalization. Moreover, even within a single domain, land-cover classes present highly complex and diverse intraclass distributions that cannot be effectively captured by a single class representation, further increasing the challenge of generalization. To this end, we propose a collaborative framework integrating local pixel-level contrast and global Gaussian multiprototype bidirectional alignment. At the local level, we introduce probability-masked contrastive learning, which adaptively increases the sampling probability of minority classes to mitigate the class imbalance issue. Meanwhile, pixel contrastive learning is incorporated to enhance the robustness to cross-domain variations in appearance and texture. At the global level, we employ a Gaussian mixture model to represent each source-domain class with multiple Gaussian prototypes rather than a single one, thereby yielding richer and more fine-grained class representations. Building on this, a bidirectional alignment strategy is proposed. Concretely, the forward alignment serves multiprototypes as semantic anchors that progressively guide target-domain features to align with the source-domain class distributions, reducing the intraclass variance. Meanwhile, the reverse alignment dynamically refines the prototypes to increase anchor accuracy, further enhancing the stability and discriminability of cross-domain alignment. Experimental results on the widely used ISPRS and LoveDA datasets demonstrate the superiority of our proposed method over state-of-the-art approaches.