# Pseudo-Zernike Moments Based Sparse Representations for SAR Image Classification

Source: [Pseudo-Zernike Moments Based Sparse Representations for SAR Image Classification](<https://www.sota2.com/research/paper/68fa7f8b1e209131cea9a6ac>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:50:43.062Z

Authors: Shahzad Gishkori, Bernard Mulgrew

Published: 2017-10-25T11:32:20.000Z

## Abstract

We propose radar image classification via pseudo-Zernike moments based sparse representations. We exploit invariance properties of pseudo-Zernike moments to augment redundancy in the sparsity representative dictionary by introducing auxiliary atoms. We employ complex radar signatures. We prove the validity of our proposed methods on the publicly available MSTAR dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1710.09175>)

[PDF](<https://arxiv.org/pdf/1710.09175.pdf>)