In recent years, deep learning has emerged as a powerful approach in remote sensing applications, particularly in segmentation and classification techniques that play a crucial role in extracting significant land features from satellite and aerial imagery. However, only a limited number of papers have discussed the use of deep learning for interactive segmentation in land cover classification tasks. In this study, we aim to bridge the gap between interactive segmentation and remote sensing image analysis by conducting a benchmark study on various deep learning-based interactive segmentation models. We assessed the performance of five state-of-the-art interactive segmentation methods (SimpleClick, FocalClick, Iterative Click Loss (ICL), Reviving Iterative Training with Mask Guidance for Interactive Segmentation (RITM), and Segment Anything (SAM)) on two high-resolution aerial imagery datasets. To enhance the segmentation results without requiring multiple models, we introduced the Cascade-Forward Refinement (CFR) approach, an innovative inference strategy for interactive segmentation. We evaluated these interactive segmentation methods on various land cover types, object sizes, and band combinations in remote sensing. Surprisingly, the popularly discussed method, SAM, proved to be ineffective for remote sensing images. Conversely, the point-based approach used in the SimpleClick models consistently outperformed the other methods in all experiments. Building upon these findings, we developed a dedicated online tool called RSISeg for interactive segmentation of remote sensing data. RSISeg incorporates a well-performing interactive model, fine-tuned with remote sensing data. Additionally, we integrated the SAM model into this tool. Compared to existing interactive segmentation tools, RSISeg offers strong interactivity, modifiability, and adaptability to remote sensing data.

翻译：近年来，深度学习已成为遥感应用中的强大方法，尤其是在从卫星和航空影像中提取重要地物特征的分割与分类技术中发挥着关键作用。然而，仅有少量论文探讨了深度学习在地物覆盖分类任务中用于交互式分割。本研究旨在通过针对多种基于深度学习的交互式分割模型开展基准研究，弥合交互式分割与遥感图像分析之间的差距。我们在两个高分辨率航空影像数据集上评估了五种最先进的交互式分割方法（SimpleClick、FocalClick、迭代点击损失（ICL）、基于掩码引导的交互式分割迭代训练（RITM）以及Segment Anything（SAM））的性能。为在不需多个模型的情况下提升分割结果，我们引入了级联前向细化（CFR）方法，这是一种针对交互式分割的创新推理策略。我们对这些交互式分割方法在遥感中的不同地物类型、目标尺寸及波段组合上进行了评估。令人惊讶的是，广受讨论的SAM方法在遥感图像上效果不佳。相反，SimpleClick模型所采用的基于点的交互方式在所有实验中均持续优于其他方法。基于这些发现，我们开发了名为RSISeg的专用在线工具，用于遥感数据的交互式分割。RSISeg集成了一个经遥感数据微调后表现优异的交互式模型。此外，我们将SAM模型也集成到该工具中。与现有交互式分割工具相比，RSISeg具有强交互性、可修改性以及对遥感数据的良好适应性。