Similarity-based data mining for online domain adaptation of a sonar ATR system

TL;DR

This paper introduces a similarity-based data mining approach for online domain adaptation of sonar ATR systems, enabling rapid adaptation to new environments with limited data and outperforming traditional methods.

Contribution

It proposes a novel data-selection method based on visual similarity for online fine-tuning of ATR systems, addressing resource and time constraints in underwater applications.

Findings

Outperforms traditional hard-mining methods in simulated environments

Enables rapid adaptation to unseen environments

Improves ATR performance with limited training data

Abstract

Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems. This problem is often addressed with domain adaptation techniques, however the currently existing methods fail to satisfy the constraints of resource and time-limited underwater systems. We propose to address this issue via an online fine-tuning of the ATR algorithm using a novel data-selection method. Our proposed data-mining approach relies on visual similarity and outperforms the traditionally employed hard-mining methods. We present a comparative performance analysis in a wide range of simulated environments and highlight the benefits of using our method for the rapid adaptation to previously unseen environments.