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Mathematics
Volume 13
Issue 3
10.3390/math13030457
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Optimization of Autoencoders for Speckle Reduction in SAR Imagery Through Variance Analysis and Quantitative Evaluation

by
Ahmed Alejandro Cardona-Mesa
1,
Rubén Darío Vásquez-Salazar
2,
Jean P. Diaz-Paz
2,
Henry O. Sarmiento-Maldonado
2,
Luis Gómez
3 and
Carlos M. Travieso-González
4,*
1
Faculty of Sciences and Humanities, Institución Universitaria Digital de Antioquia, 55th Av, 42-90, Medellín 050012, Colombia
2
Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín 050022, Colombia
3
Electronic Engineering and Automatic Control Department, IUCES, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
4
Signals and Communications Department, IDeTIC, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(3), 457; https://doi.org/10.3390/math13030457
Submission received: 31 December 2024 / Revised: 22 January 2025 / Accepted: 28 January 2025 / Published: 30 January 2025
(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition)
Review Reports Versions Notes

Abstract

Speckle reduction in Synthetic Aperture Radar (SAR) images is a crucial challenge for effective image analysis and interpretation in remote sensing applications. This study proposes a novel deep learning-based approach using autoencoder architectures for SAR image despeckling, incorporating analysis of variance (ANOVA) for hyperparameter optimization. The research addresses significant gaps in existing methods, such as the lack of rigorous model evaluation and the absence of systematic optimization techniques for deep learning models in SAR image processing. The methodology involves training 240 autoencoder models on real-world SAR data, with performance metrics evaluated using Mean Squared Error (MSE), Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Equivalent Number of Looks (ENL). By employing Pareto frontier optimization, the study identifies models that effectively balance denoising performance with the preservation of image fidelity. The results demonstrate substantial improvements in speckle reduction and image quality, validating the effectiveness of the proposed approach. This work advances the application of deep learning in SAR image denoising, offering a comprehensive framework for model evaluation and optimization.
Keywords: despeckle; Synthetic Aperture Radar; deep learning; autoencoder; analysis of variance; hyperparameter despeckle; Synthetic Aperture Radar; deep learning; autoencoder; analysis of variance; hyperparameter

Share and Cite

MDPI and ACS Style

Cardona-Mesa, A.A.; Vásquez-Salazar, R.D.; Diaz-Paz, J.P.; Sarmiento-Maldonado, H.O.; Gómez, L.; Travieso-González, C.M. Optimization of Autoencoders for Speckle Reduction in SAR Imagery Through Variance Analysis and Quantitative Evaluation. Mathematics 2025, 13, 457. https://doi.org/10.3390/math13030457

AMA Style

Cardona-Mesa AA, Vásquez-Salazar RD, Diaz-Paz JP, Sarmiento-Maldonado HO, Gómez L, Travieso-González CM. Optimization of Autoencoders for Speckle Reduction in SAR Imagery Through Variance Analysis and Quantitative Evaluation. Mathematics. 2025; 13(3):457. https://doi.org/10.3390/math13030457

Chicago/Turabian Style

Cardona-Mesa, Ahmed Alejandro, Rubén Darío Vásquez-Salazar, Jean P. Diaz-Paz, Henry O. Sarmiento-Maldonado, Luis Gómez, and Carlos M. Travieso-González. 2025. "Optimization of Autoencoders for Speckle Reduction in SAR Imagery Through Variance Analysis and Quantitative Evaluation" Mathematics 13, no. 3: 457. https://doi.org/10.3390/math13030457

APA Style

Cardona-Mesa, A. A., Vásquez-Salazar, R. D., Diaz-Paz, J. P., Sarmiento-Maldonado, H. O., Gómez, L., & Travieso-González, C. M. (2025). Optimization of Autoencoders for Speckle Reduction in SAR Imagery Through Variance Analysis and Quantitative Evaluation. Mathematics, 13(3), 457. https://doi.org/10.3390/math13030457

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