Deep Learning-Based Feature Matching Algorithm for Multi-Beam and Side-Scan Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 4; p. 675
• Main Authors: Fu, Yu, Luo, Xiaowen, Qin, Xiaoming, Wan, Hongyang, Cui, Jiaxin, Huang, Zepeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Accuracy; Acoustics; Algorithms; Backscattering; Cultural heritage; Deep learning; Depth measurement; Echo sounding; feature matching; Image quality; image registration; Image resolution; Machine learning; Mapping; Matching; Methods; multi-beam system; Neural networks; Ocean bottom; Ocean floor; Registration; Root-mean-square errors; Shipwrecks; Side scan sonar; side-scan system; Signal processing; Sonar; Topography
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17040675

Side-scan sonar and multi-beam echo sounder (MBES) are the most widely used underwater surveying tools in marine mapping today. The MBES offers high accuracy in depth measurement but is limited by low imaging resolution due to beam density constraints. Conversely, side-scan sonar provides high-resolution backscatter intensity images but lacks precise positional information and often suffers from distortions. Thus, MBES and side-scan images complement each other in depth accuracy and imaging resolution. To obtain high-quality seafloor topography images in practice, matching between MBES and side-scan images is necessary. However, due to the significant differences in content and resolution between MBES depth images and side-scan backscatter images, they represent a typical example of heterogeneous images, making feature matching difficult with traditional image matching methods. To address this issue, this paper proposes a feature matching network based on the LoFTR algorithm, utilizing the intermediate layers of the ResNet-50 network to extract shared features between the two types of images. By leveraging self-attention and cross-attention mechanisms, the features of the MBES and side-scan images are combined, and a similarity matrix of the two modalities is calculated to achieve mutual matching. Experimental results show that, compared to traditional methods, the proposed model exhibits greater robustness to noise interference and effectively reduces noise. It also overcomes challenges, such as large nonlinear differences, significant geometric distortions, and high matching difficulty between the MBES and side-scan images, significantly improving the optimized image matching results. The matching error RMSE has been reduced to within six pixels, enabling the accurate matching of multi-beam and side-scan images.