Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation

Abstract

Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings. Large-scale remote sensing images often have multiple target domains in practical applications, and the simple extension of single-target UDA models to multiple target domains is unstable and costly. Multi-target unsupervised domain adaptation (MTUDA) is a more practical scenario that has great potential for solving the problem of crossing multiple domains in remote sensing images. However, existing MTUDA models neglect to learn and control the private features of the target domain, leading to missing information and negative migration. To solve these problems, this paper proposes a multibranch unsupervised domain adaptation network (MBUDA) for orchard area segmentation. The multibranch framework aligns multiple domain features, while preventing private features from interfering with training. We introduce multiple ancillary classifiers to help the model learn more robust latent target domain data representations. Additionally, we propose an adaptation enhanced learning strategy to reduce the distribution gaps further and enhance the adaptation effect. To evaluate the proposed method, this paper utilizes two settings with different numbers of target domains. On average, the proposed method achieves a high IoU gain of 7.47% over the baseline (single-target UDA), reducing costs and ensuring segmentation model performance in multiple target domains.