Semantic segmentation of remote sensing imagery plays a pivotal role in extracting precise information for diverse down-stream applications. Recent development of the Segment Anything Model (SAM), an advanced general-purpose segmentation model, has revolutionized this field, presenting new avenues for accurate and efficient segmentation. However, SAM is limited to generating segmentation results without class information. Consequently, the utilization of such a powerful general vision model for semantic segmentation in remote sensing images has become a focal point of research. In this paper, we present a streamlined framework aimed at leveraging the raw output of SAM by exploiting two novel concepts called SAM-Generated Object (SGO) and SAM-Generated Boundary (SGB). More specifically, we propose a novel object loss and further introduce a boundary loss as augmentative components to aid in model optimization in a general semantic segmentation framework. Taking into account the content characteristics of SGO, we introduce the concept of object consistency to leverage segmented regions lacking semantic information. By imposing constraints on the consistency of predicted values within objects, the object loss aims to enhance semantic segmentation performance. Furthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object. Experimental results on two well-known datasets, namely ISPRS Vaihingen and LoveDA Urban, demonstrate the effectiveness of our proposed method. The source code for this work will be accessible at https://github.com/sstary/SSRS.

翻译：遥感影像语义分割在提取面向多样化下游应用的精准信息中发挥着关键作用。通用分割模型Segment Anything Model (SAM)的近期发展革新了这一领域，为精确高效的语义分割开辟了新路径。然而，SAM仅限于生成不含类别信息的分割结果。因此，如何利用这一强大的通用视觉模型实现遥感影像语义分割成为研究焦点。本文提出一种精简框架，通过挖掘两种新概念——SAM生成对象(SAM-Generated Object, SGO)和SAM生成边界(SAM-Generated Boundary, SGB)——来充分利用SAM的原始输出。具体而言，我们在通用语义分割框架中引入一种新型对象损失函数及边界损失函数作为辅助优化组件。基于SGO的内容特性，我们提出对象一致性概念，以利用缺乏语义信息的分割区域：通过约束对象内部预测值的一致性，对象损失函数旨在提升语义分割性能。同时，边界损失函数利用SGB的独特特征，引导模型聚焦对象的边界信息。在ISPRS Vaihingen与LoveDA Urban两个公开数据集上的实验结果表明了本文方法的有效性。本工作的源代码将公开于https://github.com/sstary/SSRS。