Semantic segmentation of remote sensing images has attracted much attention for its wide applications. While deep learning models have shown impressive performance in this task, challenges arise when applying them to data from other domains without fine-tuning, due to domain gaps. Domain adaptation (DA) has emerged as a solution to bridge this gap. Existing works mainly focus on unsupervised DA (UDA), which lags far behind fully supervised models. However, active DA (ADA) methods focused on natural images face challenges when applied to remote sensing images due to pronounced domain gaps and error unawareness problems. In this work, we propose a novel error-aware DA framework for remote sensing imagery semantic segmentation, called EasySeg, via interactive learning and active learning. First, we introduce a point-level labeling strategy, named “See-First-Ask-Later” (SFAL), combining both interactive and active learning manners, allowing obvious errors and information-rich pixels to be annotated easily and efficiently. Then, we introduce an interactive semantic segmentation network (ISS-Net), which can perform automatic semantic segmentation and interactive refinement. Based on the acquired target point-level labels, ISS-Net generates dense and accurate pseudo-labels to enhance DA performance through retraining with consistency regularization. Comprehensive experiments on two tasks demonstrate that our method outperforms the state-of-the-art ADA methods in terms of overall accuracy (OA), mean accuracy (MA), $F1$ score, and mean intersection of union (mIoU) with lower labeling costs, even surpassing some fully supervised models. The source code of EasySeg is freely available at https://github.com/Yangliangzhe/EasySeg .

Semantic segmentation of remote sensing (RS) images, which is a fundamental research topic, classifies each pixel in an image. It plays an essential role in many downstream RS areas, such as land-cover mapping, road extraction, traffic monitoring, and so on. Recently, although deep-learning-based methods have shown their dominance in automatic semantic segmentation of RS imagery, the performance of these existing methods has relied heavily on large amounts of high-quality training data, which are usually hard to obtain in practice. Moreover, human-in-the-loop semantic segmentation of RS imagery cannot be completely replaced by automatic segmentation models, since automatic models are prone to error in some complex scenarios. To address these issues, in this paper, we propose an improved, smart, and interactive segmentation model, DRE-Net, for RS images. The proposed model facilitates humans’ performance of segmentation by simply clicking a mouse. Firstly, a dynamic radius-encoding (DRE) algorithm is designed to distinguish the purpose of each click, such as a click for the selection of a segmentation outline or for fine-tuning. Secondly, we propose an incremental training strategy to cause the proposed model not only to converge quickly, but also to obtain refined segmentation results. Finally, we conducted comprehensive experiments on the Potsdam and Vaihingen datasets and achieved 9.75% and 7.03% improvements in $No{C}_{95}$ compared to the state-of-the-art results, respectively. In addition, our DRE-Net can improve the convergence and generalization of a network with a fast inference speed.