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

• URL: http://arxiv.org/abs/2604.17920v1
• Date: Mon, 20 Apr 2026 07:57:11 GMT
• Title: Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery
• Abstract summary: This paper explores how general-purpose vision foundation models can enable zero-shot ship instance segmentation in Synthetic Aperture Radar (SAR) imagery.<n>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.<n>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.
• Score: 4.3991363299650335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation [51.94680303125737]
  We present CrossEarth-SAR, the first billion-scale SAR vision foundation model built upon a novel physics-guided sparse mixture-of-experts (MoE) architecture.<n>To facilitate large-scale pre-training, we develop CrossEarth-SAR-200K, a weakly and fully supervised dataset that unifies public and private SAR imagery.<n>We also introduce a benchmark suite comprising 22 sub-benchmarks across 8 distinct domain gaps, establishing the first unified standard for domain generalization semantic segmentation on SAR imagery.
  arXiv  Detail & Related papers  (2026-03-12T14:52:35Z)
• SARMAE: Masked Autoencoder for SAR Representation Learning [17.36199520462285]
  We propose SARMAE, a Noise-Aware Masked Autoencoder for self-supervised SAR representation learning.<n>SARMAE injects SAR-specific speckle noise into masked autoencoders to facilitate noise-aware and robust representation learning.<n>Experiments across multiple SAR datasets demonstrate that SARMAE achieves state-of-the-art performance on classification, detection, and segmentation tasks.
  arXiv  Detail & Related papers  (2025-12-18T15:10:19Z)
• SARCLIP: A Vision Language Foundation Model for Semantic Understanding and Target Recognition in SAR Imagery [46.87845911116779]
  We introduce SARCLIP, the first vision language foundation model tailored for the SAR domain.<n>SARCLIP is trained using a contrastive vision language learning approach by domain transferring strategy.<n>Experiments on image-text retrieval and zero-shot classification tasks demonstrate the superior performance of SARCLIP.
  arXiv  Detail & Related papers  (2025-10-26T13:04:50Z)
• Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images [51.74614065919118]
  This paper introduces SegEarth-OV, the first framework for annotation-free open-vocabulary segmentation of RS images.<n>We propose SimFeatUp, a universal upsampler that robustly restores high-resolution spatial details from coarse features.<n>We also present a simple yet effective Global Bias Alleviation operation to subtract the inherent global context from patch features.
  arXiv  Detail & Related papers  (2025-08-25T14:22:57Z)
• SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling [41.24071764578782]
  Object detection in satellite-borne Synthetic Aperture Radar imagery holds immense potential in tasks such as urban monitoring and disaster response.<n>The detection of small objects in satellite-borne SAR images poses a particularly intricate problem, because of the technology's relatively low spatial resolution and inherent noise.<n>In this paper, we introduce TRANSAR, a novel self-supervised end-to-end vision transformer-based SAR object detection model.
  arXiv  Detail & Related papers  (2025-04-17T19:44:05Z)
• Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation [67.23953699167274]
  Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO)<n>In EO, this challenge is amplified by the redundancy and heavy-tailed distributions common in satellite imagery.<n>We propose a dynamic dataset pruning strategy designed to improve SSL pre-training by maximizing dataset diversity and balance.
  arXiv  Detail & Related papers  (2025-04-09T15:13:26Z)
• SARATR-X: Toward Building A Foundation Model for SAR Target Recognition [22.770010893572973]
  We make the first attempt towards building a foundation model for SAR ATR, termed SARATR-X.<n>SARATR-X learns generalizable representations via self-supervised learning (SSL) and provides a cornerstone for label-efficient model adaptation to generic SAR target detection and classification tasks.<n>SARATR-X is trained on 0.18 M unlabelled SAR target samples, which are curated by combining contemporary benchmarks and constitute the largest publicly available dataset till now.
  arXiv  Detail & Related papers  (2024-05-15T14:17:44Z)
• SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection [79.23689506129733]
  We establish a new benchmark dataset and an open-source method for large-scale SAR object detection.<n>Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets.<n>To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created.
  arXiv  Detail & Related papers  (2024-03-11T09:20:40Z)
• Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery [30.590315753622132]
  We explore the "hidden" ability of large-scale pre-trained image generation models, such as Stable Diffusion and Imagen, in non-visible light domains. We propose a 2-stage low-rank adaptation method, and we call it 2LoRA. In the first stage, the model is adapted using aerial-view regular image data (whose structure matches SAR), followed by the second stage where the base model from the first stage is further adapted using SAR modality data.
  arXiv  Detail & Related papers  (2023-11-29T09:48:01Z)
• Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture [23.375515181854254]
  Self-Supervised Learning (SSL) methods can achieve various SAR Automatic Target Recognition (ATR) tasks with pre-training in large-scale unlabeled data. SSL aims to construct supervision signals directly from the data, which minimizes the need for expensive expert annotation. This study investigates an effective SSL method for SAR ATR, which can pave the way for a foundation model in SAR ATR.
  arXiv  Detail & Related papers  (2023-11-26T01:05:55Z)
• SAR-ShipNet: SAR-Ship Detection Neural Network via Bidirectional Coordinate Attention and Multi-resolution Feature Fusion [7.323279438948967]
  This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network. We propose a SAR-ship detection neural network (call SAR-ShipNet for short), by newly developing Bidirectional Coordinate Attention (BCA) and Multi-resolution Feature Fusion (MRF) based on CenterNet. Experimental results on the public SAR-Ship dataset show that our SAR-ShipNet achieves competitive advantages in both speed and accuracy.
  arXiv  Detail & Related papers  (2022-03-29T12:27:04Z)
• Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection [50.53262868498824]
  Shape deformation of targets in SAR image due to random orientation and partial information loss is an essential challenge in SAR ship detection. We propose a data augmentation method to train a deep network that is robust to partial information loss within the targets.
  arXiv  Detail & Related papers  (2022-02-14T07:01:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.