Pseudo-Zernike Based Multi-Pass Automatic Target Recognition From Multi-Channel SAR

TL;DR

This paper introduces a novel ATR method using pseudo-Zernike moments on multi-channel SAR data, leveraging diversities for high-confidence recognition with low computational complexity, validated on real data.

Contribution

It presents a new ATR approach combining pseudo-Zernike moments with multi-channel multi-pass SAR data, exploiting diversities and invariance for improved performance.

Findings

Effective in different configurations and data sources

Achieves high confidence ATR with low computational complexity

Validated on real SAR data

Abstract

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provide the opportunity to exploit diversities in order to mitigate uncertainty. For the specific challenge of Automatic Target Recognition (ATR) from radar platforms, both channel (e.g. polarization) and spatial diversity can provide useful information for such a specific and critical task. In this paper the use of pseudo-Zernike moments applied to multi-channel multi-pass data is presented exploiting diversities and invariant properties leading to high confidence ATR, small computational complexity and data transfer requirements. The effectiveness of the proposed approach, in different configurations and data source availability is demonstrated using real data.