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

arXiv: https://arxiv.org/abs/1404.1682 · HTML: https://mlsanity.com/paper/1404.1682/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2014.

## Built on by (most cited first, 1 of 1)

- [Weakly Contrastive Learning via Batch Instance Discrimination and Feature Clustering for Small Sample SAR ATR](https://mlsanity.com/paper/2408.03627/) (2024)

## Abstract (arXiv)

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.