Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar Imagery
The speckle is a granular undesired pattern present in Synthetic-Aperture Radar (SAR) imagery. Despeckling has been an active field of research during the last decades, with approaches from local filters to non-local filters that calculate the new value of a pixel according to characteristics of oth...
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/828 |
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| author | Ahmed Alejandro Cardona-Mesa Rubén Darío Vásquez-Salazar Carlos M. Travieso-González Luis Gómez |
| author_facet | Ahmed Alejandro Cardona-Mesa Rubén Darío Vásquez-Salazar Carlos M. Travieso-González Luis Gómez |
| author_sort | Ahmed Alejandro Cardona-Mesa |
| collection | DOAJ |
| description | The speckle is a granular undesired pattern present in Synthetic-Aperture Radar (SAR) imagery. Despeckling has been an active field of research during the last decades, with approaches from local filters to non-local filters that calculate the new value of a pixel according to characteristics of other pixels that are not close, the more advanced paradigms based on deep learning, and the newer based on generative artificial intelligence. For the latter, it is necessary to have a large enough labeled dataset for training and validation. In this study, we propose using a dataset designed entirely from actual SAR imagery, calculated by multitemporal fusion operations to generate a ground truth reference, which will yield the models to be trained with the actual speckle patterns in the noisy images. Then, a comparative analysis of the impacts of including the generative capacity in the models is performed through visual and quantitative assessment. From the findings, it is concluded that the use of generative artificial intelligence with actual speckle exhibits notable efficiency compared to other approaches, which makes this a promising path for research in the context of SAR imagery. |
| format | Article |
| id | doaj-art-e5dc9a3c5e264944bfc8d126dca77d8a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e5dc9a3c5e264944bfc8d126dca77d8a2025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-02-0117582810.3390/rs17050828Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar ImageryAhmed Alejandro Cardona-Mesa0Rubén Darío Vásquez-Salazar1Carlos M. Travieso-González2Luis Gómez3Faculty of Sciences and Humanities, Institución Universitaria Digital de Antioquia, 55th Av, 42-90, Medellín 050012, ColombiaFaculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín 050022, ColombiaSignals and Communications Department, IDeTIC, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, SpainElectronic Engineering and Automatic Control Department, IUCES, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, SpainThe speckle is a granular undesired pattern present in Synthetic-Aperture Radar (SAR) imagery. Despeckling has been an active field of research during the last decades, with approaches from local filters to non-local filters that calculate the new value of a pixel according to characteristics of other pixels that are not close, the more advanced paradigms based on deep learning, and the newer based on generative artificial intelligence. For the latter, it is necessary to have a large enough labeled dataset for training and validation. In this study, we propose using a dataset designed entirely from actual SAR imagery, calculated by multitemporal fusion operations to generate a ground truth reference, which will yield the models to be trained with the actual speckle patterns in the noisy images. Then, a comparative analysis of the impacts of including the generative capacity in the models is performed through visual and quantitative assessment. From the findings, it is concluded that the use of generative artificial intelligence with actual speckle exhibits notable efficiency compared to other approaches, which makes this a promising path for research in the context of SAR imagery.https://www.mdpi.com/2072-4292/17/5/828synthetic-aperture radar (SAR)remote sensingdeep learningdespecklinggenerative artificial intelligence |
| spellingShingle | Ahmed Alejandro Cardona-Mesa Rubén Darío Vásquez-Salazar Carlos M. Travieso-González Luis Gómez Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar Imagery Remote Sensing synthetic-aperture radar (SAR) remote sensing deep learning despeckling generative artificial intelligence |
| title | Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar Imagery |
| title_full | Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar Imagery |
| title_fullStr | Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar Imagery |
| title_full_unstemmed | Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar Imagery |
| title_short | Comparative Analysis of Despeckling Filters Based on Generative Artificial Intelligence Trained with Actual Synthetic Aperture Radar Imagery |
| title_sort | comparative analysis of despeckling filters based on generative artificial intelligence trained with actual synthetic aperture radar imagery |
| topic | synthetic-aperture radar (SAR) remote sensing deep learning despeckling generative artificial intelligence |
| url | https://www.mdpi.com/2072-4292/17/5/828 |
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