Classifying Multi-channel UWB SAR Imagery via Tensor Sparsity Learning Techniques

TL;DR

This paper introduces three novel tensor sparsity techniques for classifying multi-channel UWB SAR imagery, improving target detection accuracy amidst clutter and noise by exploiting polarization, aspect angle, and multi-look data.

Contribution

The paper develops new tensor sparsity models and dictionary learning methods tailored for multi-channel SAR data, enhancing classification performance over traditional approaches.

Findings

Tensor sparsity models outperform traditional methods in accuracy.

Proposed techniques effectively utilize polarization and aspect angle information.

Experimental results validate improvements on simulated and real SAR datasets.

Abstract

Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g. bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data but also take advantage of the polarization diversity and the aspect angle dependence information from multi-channel SAR data. First, the traditional sparse representation-based classification (SRC) is generalized to exploit shared information of classes and various sparsity structures of tensor coefficients for multi-channel data. Corresponding tensor dictionary learning models are consequently proposed to enhance classification accuracy. Lastly, a new tensor sparsity model is proposed to model responses from multiple consecutive looks of objects, which is a unique characteristic of the dataset we consider. Extensive experimental results on a high-fidelity electromagnetic simulated dataset and radar data collected from the U.S. Army Research Laboratory side-looking SAR demonstrate the advantages of proposed tensor sparsity models.