Learn From Segment Anything Model: Local Region Homogenizing for Cross-Domain Remote Sensing Image Segmentation

Wang Liu, Puhong Duan, Zhuojun Xie, Xudong Kang, Shutao Li

Abstract: Unsupervised domain adaption (UDA) has gained popularity in narrowing performance gaps across domains in remote sensing image semantic segmentation (RSISS). However, current UDA methods suffer from serious noisy pseudo-labels, adversely affecting domain adaptation performance. In this work, a local region homogenizing domain adaptation method (RegDA) is proposed to tackle this issue. Specifically, a generalized segment anything model (SAM) is utilized to obtain the semantic-consistent regions for the images in the target domain. Furthermore, a pixel-level voting scheme is proposed to get the semantic label for each local region and assign it to each pixel within this region. In this way, more reliable pseudo-labels are obtained and domain adaptation performance is improved. Experiment results on ISPRS datasets demonstrate that the proposed RegDA outperforms previous UDA approaches for RSISS. The code will be available at https://github.com/StuLiu/RegDA.