The development of high-resolution remote sensing satellites has provided
great convenience for research work related to remote sensing. Segmentation and
extraction of specific targets are essential tasks when facing the vast and
complex remote sensing images. Recently, the introduction of Segment Anything
Model (SAM) provides a universal pre-training model for image segmentation
tasks. While the direct application of SAM to remote sensing image segmentation
tasks does not yield satisfactory results, we propose RSAM-Seg, which stands
for Remote Sensing SAM with Semantic Segmentation, as a tailored modification
of SAM for the remote sensing field and eliminates the need for manual
intervention to provide prompts. Adapter-Scale, a set of supplementary scaling
modules, are proposed in the multi-head attention blocks of the encoder part of
SAM. Furthermore, Adapter-Feature are inserted between the Vision Transformer
(ViT) blocks. These modules aim to incorporate high-frequency image information
and image embedding features to generate image-informed prompts. Experiments
are conducted on four distinct remote sensing scenarios, encompassing cloud
detection, field monitoring, building detection and road mapping tasks . The
experimental results not only showcase the improvement over the original SAM
and U-Net across cloud, buildings, fields and roads scenarios, but also
highlight the capacity of RSAM-Seg to discern absent areas within the ground
truth of certain datasets, affirming its potential as an auxiliary annotation
method. In addition, the performance in few-shot scenarios is commendable,
underscores its potential in dealing with limited datasets.

由于高分辨率遥感卫星的发展，遥感相关研究工作获得了极大的便利。SAM 引入了一种普适的预训练模型用于图像分割任务，但是直接应用于遥感图像分割任务效果欠佳。为此，我们提出了 RSAM-Seg，即在 SAM 的基础上进行了适应性修改，消除了对手动干预提供提示的需求。在 SAM 的编码器部分的多头注意力块中，我们提出了 Adapter-Scale 和 Adapter-Feature 模块，它们旨在将高频图像信息和图像嵌入特征融入生成的图像引导提示中。在包括云检测、场地监测、建筑物检测和道路制图任务的四个不同遥感场景上进行了实验证明，RSAM-Seg 不仅改善了原始 SAM 和 U-Net 在云、建筑物、场地和道路场景中的效果，还突出了其作为辅助注释方法的潜力，可以识别某些数据集的真实值中的缺失区域。此外，在少样本情况下的表现也值得称赞，凸显了其在处理有限数据集时的潜力。