# Improving Sonar Image Patch Matching via Deep Learning

arXiv: https://arxiv.org/abs/1709.02150 · HTML: https://mlsanity.com/paper/1709.02150/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [The Marine Debris Forward-Looking Sonar Datasets](https://mlsanity.com/paper/2503.22880/) (2025)
- [Forward-Looking Sonar Patch Matching: Modern CNNs, Ensembling, and Uncertainty](https://mlsanity.com/paper/2108.01066/) (2021)
- [A Matching Algorithm based on Image Attribute Transfer and Local Features for Underwater Acoustic and Optical Images](https://mlsanity.com/paper/2108.12151/) (2021)
- [Nonlinear Intensity Sonar Image Matching based on Deep Convolution Features](https://mlsanity.com/paper/2111.08994/) (2021)
- [Nonlinear Intensity Underwater Sonar Image Matching Method Based on Phase Information and Deep Convolution Features](https://mlsanity.com/paper/2111.15514/) (2021)

## Abstract (arXiv)

Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good sonar image matching capabilities for tasks such as tracking, simultaneous localization and mapping (SLAM) and some cases of object detection/recognition. We propose the use of Convolutional Neural Networks (CNN) to learn a matching function that can be trained from labeled sonar data, after pre-processing to generate matching and non-matching pairs. In a dataset of 39K training pairs, we obtain 0.91 Area under the ROC Curve (AUC) for a CNN that outputs a binary classification matching decision, and 0.89 AUC for another CNN that outputs a matching score. In comparison, classical keypoint matching methods like SIFT, SURF, ORB and AKAZE obtain AUC 0.61 to 0.68. Alternative learning methods obtain similar results, with a Random Forest Classifier obtaining AUC 0.79, and a Support Vector Machine resulting in AUC 0.66.