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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 heavilyrelies on prior manual guidance, including points, boxes, and coarse-grainedmasks. Furthermore, its performance in remote sensing image segmentation tasksremains largely unexplored and unproven. In this paper, we aim to develop anautomated instance segmentation approach for remote sensing images, based onthe foundational SAM model and incorporating semantic category information.Drawing inspiration from prompt learning, we propose a method to learn thegeneration of appropriate prompts for SAM. This enables SAM to producesemantically discernible segmentation results for remote sensing images, aconcept we have termed RSPrompter. We also propose several ongoing derivativesfor instance segmentation tasks, drawing on recent advancements within the SAMcommunity, and compare their performance with RSPrompter. Extensiveexperimental results, derived from the WHU building, NWPU VHR-10, and SSDDdatasets, validate the effectiveness of our proposed method. The code for ourmethod is publicly available at kychen.me/RSPrompter.

RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model