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Comparative Analysis of Frequency-Domain Filters for Speckle Reduction in PolSAR Imagery

by Bhakti Talele, Sai Gurav, Avinash Dhiran, Aarya Shinde, Varsha Turkar, Yogesh Agarwadkar, Mugdha Agarwadkar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 119
Year of Publication: 2026
Authors: Bhakti Talele, Sai Gurav, Avinash Dhiran, Aarya Shinde, Varsha Turkar, Yogesh Agarwadkar, Mugdha Agarwadkar
10.5120/ijca36ad1c51c577

Bhakti Talele, Sai Gurav, Avinash Dhiran, Aarya Shinde, Varsha Turkar, Yogesh Agarwadkar, Mugdha Agarwadkar . Comparative Analysis of Frequency-Domain Filters for Speckle Reduction in PolSAR Imagery. International Journal of Computer Applications. 187, 119 ( Jun 2026), 42-52. DOI=10.5120/ijca36ad1c51c577

@article{ 10.5120/ijca36ad1c51c577,
author = { Bhakti Talele, Sai Gurav, Avinash Dhiran, Aarya Shinde, Varsha Turkar, Yogesh Agarwadkar, Mugdha Agarwadkar },
title = { Comparative Analysis of Frequency-Domain Filters for Speckle Reduction in PolSAR Imagery },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2026 },
volume = { 187 },
number = { 119 },
month = { Jun },
year = { 2026 },
issn = { 0975-8887 },
pages = { 42-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number119/comparative-analysis-of-frequency-domain-filters-for-speckle-reduction-in-polsar-imagery/ },
doi = { 10.5120/ijca36ad1c51c577 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-01T03:10:02.844488+05:30
%A Bhakti Talele
%A Sai Gurav
%A Avinash Dhiran
%A Aarya Shinde
%A Varsha Turkar
%A Yogesh Agarwadkar
%A Mugdha Agarwadkar
%T Comparative Analysis of Frequency-Domain Filters for Speckle Reduction in PolSAR Imagery
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 119
%P 42-52
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In remote sensing, high-quality image data is crucial for effective analysis and interpretation. This study focuses on analyzing the impact of image quality by applying various frequency-domain filters like Gaussian, Butterworth, Chebyshev, Ideal, Elliptic, Laplacian, Extended Adaptive Wiener, Logarithmic and Homomorphic filter on the T3 components of SAR imagery. A quantitative analysis of image quality was carried out using metrics such as the Coefficient of Variation (CV), Signal-to-Noise Ratio (SNR), Equivalent Number of Looks (ENL), Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Compared to the traditionally used Lee Refined filter, the Extended Adaptive Wiener filter demonstrated improved SNR, PSNR, and SSIM, with only slight compromises in CV and ENL. While the Lee Refined filter maintained balanced performance, other frequency-domain filters tended to either over-smooth (e.g., Butterworth, Homomorphic) or underperform (e.g., Chebyshev, Elliptic, Gaussian, Ideal, Logarithmic). These findings highlight the Extended Adaptive Wiener filter as a promising approach for speckle reduction in PolSAR data, supporting improved clarity and structural preservation in remote sensing applications.

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Index Terms

Computer Science
Information Sciences

Keywords

Synthetic Aperture Radar (SAR) Frequency Domain Filtering T3 Matrix Components Image Quality Assessment De-speckling Extended Adaptive Wiener

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