Optimization of Autoencoders for Speckle Reduction in SAR Imagery Through Variance Analysis and Quantitative Evaluation
Guardat en:
| Publicat a: | Mathematics vol. 13, no. 3 (2025), p. 457 |
|---|---|
| Autor principal: | |
| Altres autors: | , , , , |
| Publicat: |
MDPI AG
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3165831698 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13030457 |2 doi | |
| 035 | |a 3165831698 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Cardona-Mesa, Ahmed Alejandro |u Faculty of Sciences and Humanities, Institución Universitaria Digital de Antioquia, 55th Av, 42-90, Medellín 050012, Colombia; <email>amhed.cardona@iudigital.edu.co</email> | |
| 245 | 1 | |a Optimization of Autoencoders for Speckle Reduction in SAR Imagery Through Variance Analysis and Quantitative Evaluation | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Image analysis | ||
| 653 | |a Performance measurement | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Interferometry | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Signal to noise ratio | ||
| 653 | |a Noise reduction | ||
| 653 | |a Synthetic aperture radar | ||
| 653 | |a Optimization | ||
| 653 | |a Effectiveness | ||
| 653 | |a Remote sensing | ||
| 653 | |a Algorithms | ||
| 653 | |a Image quality | ||
| 653 | |a Variance analysis | ||
| 653 | |a Radar imaging | ||
| 653 | |a Image processing | ||
| 653 | |a Statistical methods | ||
| 700 | 1 | |a Vásquez-Salazar, Rubén Darío |u Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín 050022, Colombia; <email>rdvasquez@elpoli.edu.co</email> (R.D.V.-S.); <email>jpdiaz@elpoli.edu.co</email> (J.P.D.-P.); <email>hosarmiento@elpoli.edu.co</email> (H.O.S.-M.) | |
| 700 | 1 | |a Diaz-Paz, Jean P |u Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín 050022, Colombia; <email>rdvasquez@elpoli.edu.co</email> (R.D.V.-S.); <email>jpdiaz@elpoli.edu.co</email> (J.P.D.-P.); <email>hosarmiento@elpoli.edu.co</email> (H.O.S.-M.) | |
| 700 | 1 | |a Sarmiento-Maldonado, Henry O |u Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín 050022, Colombia; <email>rdvasquez@elpoli.edu.co</email> (R.D.V.-S.); <email>jpdiaz@elpoli.edu.co</email> (J.P.D.-P.); <email>hosarmiento@elpoli.edu.co</email> (H.O.S.-M.) | |
| 700 | 1 | |a Gómez, Luis |u Electronic Engineering and Automatic Control Department, IUCES, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain; <email>luis.gomez@ulpgc.es</email> | |
| 700 | 1 | |a Travieso-González, Carlos M |u Signals and Communications Department, IDeTIC, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 3 (2025), p. 457 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165831698/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3165831698/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165831698/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |