Localized Dictionary design for Geometrically Robust Sonar ATR

TL;DR

This paper introduces a localized dictionary design with pose management for sonar ATR, improving robustness to geometric pose variations and outperforming existing methods in cluttered environments.

Contribution

It proposes a novel localized block-based dictionary with dictionary learning for pose-robust sonar ATR, addressing a key limitation of prior sparsity-based techniques.

Findings

Outperforms state-of-the-art SIFT and SVM methods.

Enhances robustness to geometric pose variations.

Effectively discerns background clutter in sonar images.

Abstract

Advancements in Sonar image capture have opened the door to powerful classification schemes for automatic target recognition (ATR. Recent work has particularly seen the application of sparse reconstruction-based classification (SRC) to sonar ATR, which provides compelling accuracy rates even in the presence of noise and blur. Existing sparsity based sonar ATR techniques however assume that the test images exhibit geometric pose that is consistent with respect to the training set. This work addresses the outstanding open challenge of handling inconsistently posed test sonar images relative to training. We develop a new localized block-based dictionary design that can enable geometric, i.e. pose robustness. Further, a dictionary learning method is incorporated to increase performance and efficiency. The proposed SRC with Localized Pose Management (LPM), is shown to outperform the state of the art SIFT feature and SVM approach, due to its power to discern background clutter in Sonar images.