{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Deniz Gencaga", "hasEmail": "mailto:dgencaga@gmail.com"}, "description": "In this paper, we propose a novel approach to reduce the noise in Synthetic Aperture Radar (SAR) images using particle filters. Interpretation of SAR images is a difficult problem, since they are contaminated with a multiplicative noise, which is known as the \u201cSpeckle Noise\u201d. In literature, the general approach  for removing\r\nthe speckle is to use the local statistics, which are computed in a square window. Here, we propose to use particle filters, which is a sequential Bayesian technique. The proposed method also uses the local statistics to denoise the images. Since this is a Bayesian\r\napproach, the computed statistics of the window can be exploited as a priori information. Moreover, particle filters are sequential methods, which are more appropriate to handle the heterogeneous structure of the image. Computer simulations show that the proposed method provides better edge-preserving results with\r\nsatisfactory speckle removal, when compared to the results obtained by Gamma Maximum a posteriori (MAP) filter.", "distribution": [{"@type": "dcat:Distribution", "description": "SAR image enhancement using particle filtering", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/ESA_EUSC_conference_GENCAGA_etal.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "ESA_EUSC_conference_GENCAGA_etal.pdf"}], "identifier": "DASHLINK_212", "issued": "2010-09-22", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/212/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "SAR Image Enhancement using Particle Filters"}