arXiv subjects

Can Lei

Publications and source records attributed to Can Lei.

7 recordsLinked to original sources

arXiv2026

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

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.

physics.ao-ph↗

arXiv2026

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

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.

physics.ao-ph↗

arXiv2026

Physically Motivated Knowledge Distillation for Blind Geometric Correction of Side-Scan Sonar Imagery

Side-scan sonar (SSS) imagery is susceptible to geometric distortions caused by platform motion instability, which degrade geometric consistency and limit downstream analyses such as mosaicking and perception. Conventional correction methods typically rely on navigation and attitude measurements, which are often unreliable in real ocean conditions. This unreliability necessitates blind geometric correction from a single distorted image, a highly ill-posed problem. To address this issue, we propose a physically motivated knowledge distillation framework for blind geometric correction of SSS imagery. Specifically, a teacher network is trained using paired distorted and geocoded reference images to learn distortion-related geometric differences, and this knowledge is transferred to a student network that performs correction using only a single distorted image during blind inference. To ensure physically plausible deformation estimation, we design a parametric decoder that represents distortions as row-wise affine transformations consistent with the SSS line-scanning imaging mechanism. To compensate for the absence of reference information during blind inference, a hallucination context module is introduced to approximate the teachers geometric reasoning from distorted features under a multi-level distillation scheme. In addition, a differentiable forward warping strategy is adopted to handle the non-bijective deformation characteristics of SSS imagery in an end-to-end manner. Extensive experiments on multiple datasets show that the proposed method outperforms state-of-the-art baselines and generalizes well across different platforms and acquisition conditions.

physics.ao-ph↗

arXiv2025

PhysDNet: Physics-Guided Decomposition Network of Side-Scan Sonar Imagery

Side-scan sonar (SSS) imagery is widely used for seafloor mapping and underwater remote sensing, yet the measured intensity is strongly influenced by seabed reflectivity, terrain elevation, and acoustic path loss. This entanglement makes the imagery highly view-dependent and reduces the robustness of downstream analysis. In this letter, we present PhysDNet, a physics-guided multi-branch network that decouples SSS images into three interpretable fields: seabed reflectivity, terrain elevation, and propagation loss. By embedding the Lambertian reflection model, PhysDNet reconstructs sonar intensity from these components, enabling self-supervised training without ground-truth annotations. Experiments show that the decomposed representations preserve stable geological structures, capture physically consistent illumination and attenuation, and produce reliable shadow maps. These findings demonstrate that physics-guided decomposition provides a stable and interpretable domain for SSS analysis, improving both physical consistency and downstream tasks such as registration and shadow interpretation.

physics.ao-ph↗

arXiv2025

Coarse-to-Fine Non-Rigid Registration for Side-Scan Sonar Mosaicking

Side-scan sonar mosaicking plays a crucial role in large-scale seabed mapping but is challenged by complex non-linear, spatially varying distortions due to diverse sonar acquisition conditions. Existing rigid or affine registration methods fail to model such complex deformations, whereas traditional non-rigid techniques tend to overfit and lack robustness in sparse-texture sonar data. To address these challenges, we propose a coarse-to-fine hierarchical non-rigid registration framework tailored for large-scale side-scan sonar images. Our method begins with a global Thin Plate Spline initialization from sparse correspondences, followed by superpixel-guided segmentation that partitions the image into structurally consistent patches preserving terrain integrity. Each patch is then refined by a pretrained SynthMorph network in an unsupervised manner, enabling dense and flexible alignment without task-specific training. Finally, a fusion strategy integrates both global and local deformations into a smooth, unified deformation field. Extensive quantitative and visual evaluations demonstrate that our approach significantly outperforms state-of-the-art rigid, classical non-rigid, and learning-based methods in accuracy, structural consistency, and deformation smoothness on the challenging sonar dataset.

physics.geo-ph↗

arXiv2025

A Geometrically Consistent Matching Framework for Side-Scan Sonar Mapping

Robust matching of side-scan sonar imagery remains a fundamental challenge in seafloor mapping due to view-dependent backscatter, shadows, and geometric distortion. This paper proposes a novel matching framework that combines physical decoupling and geometric consistency to enhance correspondence accuracy and consistency across viewpoints. A multi-branch network, derived from the Lambertian reflection model, decomposes raw sonar images into seabed reflectivity, terrain elevation, and acoustic path loss. The reflectivity map, serving as a stable matching domain, is used in conjunction with a training-free matching pipeline combining SuperPoint and MINIMA-LightGlue. Geometry-aware outlier rejection leverages both terrain elevation and its physically derived shadow map to further remove mismatches in acoustically occluded and topographically inconsistent regions, thereby improving registration accuracy. Quantitative and visual evaluations against traditional, CNN-, and Transformer-based state-of-the-art methods demonstrate that our method achieves lower matching error, higher geometric consistency, and greater robustness to viewpoint variations. The proposed approach provides a data-efficient, physically interpretable solution for high-precision side-scan sonar image matching in complex seafloor environments.

physics.ins-det↗

arXiv2022

SI-GAT: A method based on improved Graph Attention Network for sonar image classification

The existing sonar image classification methods based on deep learning are often analyzed in Euclidean space, only considering the local image features. For this reason, this paper presents a sonar classification method based on improved Graph Attention Network (GAT), namely SI-GAT, which is applicable to multiple types imaging sonar. This method quantifies the correlation relationship between nodes based on the joint calculation of color proximity and spatial proximity that represent the sonar characteristics in non-Euclidean space, then the KNN (K-Nearest Neighbor) algorithm is used to determine the neighborhood range and adjacency matrix in the graph attention mechanism, which are jointly considered with the attention coefficient matrix to construct the key part of the SI-GAT. This SI-GAT is superior to several CNN (Convolutional Neural Network) methods based on Euclidean space through validation of real data.

cs.CV↗