Unsupervised Domain Adaptation for Remote Sensing Data Classification Model Transfer

Abstract. In this paper, we explore the application of domain adaptation techniques for semantic land cover segmentation using aerial remote sensing data. We leverage canonical correlation and histogram matching to facilitate the transfer of knowledge from pre-trained classification models to new datasets without the need for additional labeled data. Specifically, we perform Canonical Correlation to align feature distributions between the source and target domains and Histogram Matching to enhance the correspondence of pixel distributions across datasets. The effectiveness of these domain adaptation techniques is assessed through improvements in semantic segmentation performance of the Random Forest and DeepLabV3+ classifiers on German city datasets. Our results indicate a substantial increase in segmentation accuracy when using domain adaptation methods. Furthermore, we examine the role of elevation data, represented by Normalized Digital Surface Models (NDSM), which enhances segmentation performance on unseen datasets. These findings underscore the efficacy of domain adaptation and the value of elevation data in remote sensing classification, particularly in dynamic environments where models encounter new datasets.