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

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Julkaisussa:Mathematics vol. 13, no. 3 (2025), p. 457
Päätekijä: Cardona-Mesa, Ahmed Alejandro
Muut tekijät: Vásquez-Salazar, Rubén Darío, Diaz-Paz, Jean P, Sarmiento-Maldonado, Henry O, Gómez, Luis, Travieso-González, Carlos M
Julkaistu:
MDPI AG
Aiheet:
Image analysis
Performance measurement
Deep learning
Datasets
Performance evaluation
Interferometry
Fourier transforms
Signal to noise ratio
Noise reduction
Synthetic aperture radar
Optimization
Effectiveness
Remote sensing
Algorithms
Image quality
Variance analysis
Radar imaging
Image processing
Statistical methods
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Abstrakti: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.
ISSN:2227-7390
DOI:10.3390/math13030457
Lähde:Engineering Database