{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T04:31:32Z","timestamp":1769920292088,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Polit\u00e9cnico Colombiano Jaime Isaza Cadavid"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents an analysis of different despeckling filters applied on both synthetically corrupted optical images and actual Synthetic Aperture Radar (SAR) images. Several authors use optical images as ground truth and then the images are corrupted by using a Gamma model to simulate the speckle, while other approaches use methods like multitemporal fusion to generate a ground truth using actual SAR images, which provides a result somehow equivalent to the one from the common multi look technique. Well-known filters, like local, and non-local and some of them based on artificial intelligence and deep learning, are applied to these two types of images and their performance is assessed by a quantitative analysis. One last validation is performed with a newly proposed method by using ratio images, resulting from the mathematical division (Hadamard division) of filtered and noisy images, to measure how similar the initial and the remaining speckle are by considering its Gamma distribution and divergence measurement. Our findings suggest that despeckling models relying on artificial intelligence exhibit notable efficiency, albeit concurrently displaying inflexibility when applied to particular image types based on the training dataset. Additionally, our experiments underscore the utility of the divergence measurement in ratio images in facilitating both visual inspection and quantitative evaluation of residual speckles within the filtered images.<\/jats:p>","DOI":"10.3390\/rs16162893","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T07:01:25Z","timestamp":1723100485000},"page":"2893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Analysis of Despeckling Filters Using Ratio Images and Divergence Measurement"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-2302","authenticated-orcid":false,"given":"Luis","family":"G\u00f3mez","sequence":"first","affiliation":[{"name":"Electronic Engineering and Automatic Control Department, IUCES, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5263-2569","authenticated-orcid":false,"given":"Ahmed Alejandro","family":"Cardona-Mesa","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Humanities, Instituci\u00f3n Universitaria Digital de Antioquia, 55th Av, 42-90, Medell\u00edn 050010, Colombia"},{"name":"Faculty of Engineering, Polit\u00e9cnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medell\u00edn 050022, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-8393","authenticated-orcid":false,"given":"Rub\u00e9n Dar\u00edo","family":"V\u00e1squez-Salazar","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Polit\u00e9cnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medell\u00edn 050022, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4621-2768","authenticated-orcid":false,"given":"Carlos M.","family":"Travieso-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Signals and Communications Department, IDeTIC, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1007\/s11831-021-09548-z","article-title":"A Review on SAR Image and its Despeckling","volume":"28","author":"Singh","year":"2021","journal-title":"Arch. 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