  Leveraging the extensive training data from SA-1B, the Segment Anything Model
(SAM) demonstrates remarkable generalization and zero-shot capabilities.
However, as a category-agnostic instance segmentation method, SAM heavily
relies on prior manual guidance, including points, boxes, and coarse-grained
masks. Furthermore, its performance in remote sensing image segmentation tasks
remains largely unexplored and unproven. In this paper, we aim to develop an
automated instance segmentation approach for remote sensing images, based on
the foundational SAM model and incorporating semantic category information.
Drawing inspiration from prompt learning, we propose a method to learn the
generation of appropriate prompts for SAM. This enables SAM to produce
semantically discernible segmentation results for remote sensing images, a
concept we have termed RSPrompter. We also propose several ongoing derivatives
for instance segmentation tasks, drawing on recent advancements within the SAM
community, and compare their performance with RSPrompter. Extensive
experimental results, derived from the WHU building, NWPU VHR-10, and SSDD
datasets, validate the effectiveness of our proposed method. The code for our
method is publicly available at kychen.me/RSPrompter.
