Category-Level Assignment for Cross-Domain Semantic Segmentation in Remote Sensing Images

Abstract

Deep learning-based semantic segmentation has made great progress in understanding very-high-resolution (VHR) remote sensing images (RSIs). However, large-scale applications are still limited. The main reason is that diverse imaging modes and geographical differences make it difficult to transfer a model trained in the source domain to the target domain. To solve this problem, unsupervised domain adaptation (UDA) for VHR RSIs has received some attention, but the accuracy of cross-domain semantic segmentation still needs to be improved. Currently, one reasonable proposal for improving accuracy is to take a close look at the category-level information. In this paper, we reveal an integer programming mechanism for modeling the category-level relationship between the source and target domains. The mechanism is based on the solution of the assignment problem, and thus, the proposed method is called category-level assignment for UDA (ClA-UDA). In ClA-UDA, a category-level assignment problem with additional constraints is defined for UDA tasks, and the solution is provided. Based on the solution, an assignment-based image-to-image transferring algorithm (AIT) is first proposed to transfer the source-domain images based on the style of the target-domain images. AIT minimizes a weighted discrepancy, and provides an analytical solution for the transfer. Two assignment-based alignment losses are then introduced to align the source and target domains based on the category-level relationship in a concise way. To validate the performance of ClA-UDA, three VHR remote sensing image datasets are employed, and six UDA tasks are designed. Extensive experiments are conducted, and the results demonstrate the superiority of ClA-UDA compared to the existing methods.