Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery

TL;DR

This paper presents a zero-shot approach for ship instance segmentation in SAR imagery using foundation models, combining a detector and SAM2 without pixel-level annotations, achieving high accuracy.

Contribution

It introduces a novel method that leverages a SAR-trained detector to prompt foundation models for segmentation, eliminating the need for mask annotations.

Findings

Achieves a mean IoU of 0.637 on SSDD benchmark

Detects ships with an 89.2% rate

Reduces reliance on pixel-level annotations

Abstract

Synthetic Aperture Radar (SAR) plays a critical role in maritime surveillance, yet deep learning for SAR analysis is limited by the lack of pixel-level annotations. This paper explores how general-purpose vision foundation models can enable zero-shot ship instance segmentation in SAR imagery, eliminating the need for pixel-level supervision. A YOLOv11-based detector trained on open SAR datasets localizes ships via bounding boxes, which then prompt the Segment Anything Model 2 (SAM2) to produce instance masks without any mask annotations. Unlike prior SAM-based SAR approaches that rely on fine tuning or adapters, our method demonstrates that spatial constraints from a SAR-trained detector alone can effectively regularize foundation model predictions. This design partially mitigates the optical-SAR domain gap and enables downstream applications such as vessel classification, size estimation, and wake analysis. Experiments on the SSDD benchmark achieve a mean IoU of 0.637 (89% of a fully supervised baseline) with an overall ship detection rate of 89.2%, confirming a scalable, annotation-efficient pathway toward foundation-model-driven SAR image understanding.