HARBOR: Heading Analysis and Reconstruction from Behavioral Observation and Radar

• URL: http://arxiv.org/abs/2606.14006v1
• Date: Fri, 12 Jun 2026 00:57:26 GMT
• Title: HARBOR: Heading Analysis and Reconstruction from Behavioral Observation and Radar
• Abstract summary: This work introduces a complete pipeline for transforming a single SAR image into predictive motion information.<n> AIS data are used exclusively during an offline calibration phase to derive vessel-type-dependent motion parameters.<n>A case study using real COSMO-SkyMed SAR imagery demonstrates the pipeline's ability to extract motion tendencies.
• Score: 0.29165586612027233
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Maritime situational awareness often relies on Automatic Identification System (AIS) transmissions to track vessel movements. However, in operational or conflict scenarios, these data may be unavailable due to signal loss, deliberate deactivation, or intentional spoofing. In such conditions, synthetic aperture radar (SAR) imagery becomes a critical sensing alternative for wide-area maritime monitoring, despite providing only static scene snapshots. This work introduces HARBOR (Heading Analysis and Reconstruction from Behavioral Observation and Radar), a complete pipeline for transforming a single SAR image into predictive motion information without requiring any auxiliary data source at inference time. The method begins with SAR image preprocessing to enhance and segment vessel candidates, followed by automatic detection, size-based classification, and heading estimation using skeleton geometry and local intensity patterns. AIS data are used exclusively during an offline calibration phase to derive vessel-type-dependent motion parameters, which are then applied to generate probabilistic heatmaps of candidate future vessel positions. A case study using real COSMO-SkyMed SAR imagery demonstrates the pipeline on a maritime scene in southern Brazil, showing its ability to extract motion tendencies and generate probabilistic projections of vessel positions in data-denied environments.

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