Geometric Correction of Side-Scan Sonar Images with Image-Consistent Attitude Refinement

TL;DR

This paper introduces a novel geometric correction method for side-scan sonar images that refines attitude estimates by linking distortion patterns to deformation modes, improving geometric consistency and reducing misalignments.

Contribution

It presents a new approach combining image-inferred microscopic perturbations with navigation-derived baselines for attitude refinement in SSS correction.

Findings

Reduces inter-ping misalignment and local stretching.

Improves geometric consistency in corrected images.

Effective across different datasets and environments.

Abstract

Side-scan sonar (SSS) images are susceptible to motion-induced geometric distortion, which degrades their reliability for seabed interpretation and downstream tasks. Existing correction methods either exploit image-domain consistency without adequately preserving global geometric referencing, or rely on navigation-based geocoding whose effectiveness is limited when recorded attitude and motion fail to capture ping-scale perturbations. To address this issue, we propose a geometric correction method for SSS images with image-consistent attitude refinement. The core idea is to refine the yaw-pitch sequence used in geocoding by explicitly linking stripe-wise distortion patterns in dual-sided waterfall images to geometric deformation modes. Specifically, a navigation-derived macro-scale attitude baseline is fused with image-inferred microscopic perturbations, where port-starboard symmetry is used to separate pitch-related common-mode responses from yaw-related differential-mode responses. The refined attitude is then incorporated into a physically geocoding framework with track-aligned gridding and normalized-convolution-based hole completion to generate the corrected image. Experiments on real SSS datasets from different sonar platforms and environments show that the proposed method reduces inter-ping misalignment, local stretching, and structural discontinuity, and improves local geometric consistency under both degraded-attitude and cross-dataset evaluation settings, demonstrating its effectiveness for geometrically consistent SSS correction.