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arXiv · 2607.25937

SC-Match: Scale-Space Matching with Context Consistency for Side-Scan Sonar Mapping

Can Lei·Rafael Garcia·Nuno Gracias·Hayat Rajani·Huigang Wang
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Abstract

Reliable estimation of spatial correspondences between overlapping side-scan sonar (SSS) measurements is essential for mapping, but acoustic appearance variations, weak seabed texture, repetitive patterns, and shadows make such correspondences sparse, unstable, and context-dependent. Scarce point-level annotations further limit sonar-specific training or fine-tuning of deep matching models. To this end, we propose SC-Match, a training-free scale-space matching framework with context-consistent correspondence refinement that adapts pretrained feature extraction and matching components to SSS observations without sonar-specific retraining. The framework improves correspondence reliability through scale-space representation and context-consistent refinement. For feature representation, a frozen extractor is applied to multiple observation scales, detector responses are calibrated according to the structure--texture tendency of the input SSS image, and score-aware cross-scale fusion is used to retain compact feature candidates. For correspondence refinement, adjacent local matching cases are used as neighboring contexts to verify stable fixed--moving relations and preserve non-conflicting complementary matches for alignment. Experiments on datasets acquired in different environments using different sonar platforms show that SC-Match provides more accurate correspondences and more consistent geometric alignment than representative pretrained methods, while maintaining stable behavior under unseen cross-platform acquisition conditions.

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Can Lei, Rafael Garcia, Nuno Gracias, Hayat Rajani, Huigang Wang. 2026-07-28. SC-Match: Scale-Space Matching with Context Consistency for Side-Scan Sonar Mapping. https://arxiv.org/abs/2607.25937

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