  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.
