Discriminative Sparsity for Sonar ATR

TL;DR

This paper introduces a discriminative sparsity-based method for sonar automatic target recognition that improves robustness to noise, blurring, and limited training data, demonstrating high classification accuracy on simulated data.

Contribution

It develops novel discriminative sparse representations using class-specific dictionaries, enhancing sonar ATR performance under challenging conditions.

Findings

High classification accuracy on simulated sonar data

Robustness to noise and limited training samples

Effective multi-class target recognition

Abstract

Advancements in Sonar image capture have enabled researchers to apply sophisticated object identification algorithms in order to locate targets of interest in images such as mines. Despite progress in this field, modern sonar automatic target recognition (ATR) approaches lack robustness to the amount of noise one would expect in real-world scenarios, the capability to handle blurring incurred from the physics of image capture, and the ability to excel with relatively few training samples. We address these challenges by adapting modern sparsity-based techniques with dictionaries comprising of training from each class. We develop new discriminative (as opposed to generative) sparse representations which can help automatically classify targets in Sonar imaging. Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), we obtained compelling classification rates for multi-class problems even in cases with considerable noise and sparsity in training samples.