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Inflated Rayleigh Regression Model for High Dynamic Magnitude SAR Image Modeling
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0003-0423-9927
Universidade Federal de Santa Maria, Brazil.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6834-5676
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0003-3945-8951
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2024 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 21, article id 4018705Article in journal (Refereed) Published
Abstract [en]

This letter introduces a novel regression model structure for the inflated Rayleigh distribution, which effectively models high dynamic amplitude pixel values in synthetic aperture radar (SAR) images. The proposed model estimates the mean of inflated Rayleigh distribution signals by a structure that includes a set of regressors and a link function. The inflated Rayleigh distribution combines the Rayleigh and a degenerate distribution, assigning non-null probability specifically for observed values equal to zero. Null pixel values in amplitude SAR images can be randomly distributed within the image, especially in low-intensity areas; a model capable of incorporating these values is essential to avoid changes in image statistics. Extensive evaluations are conducted using simulated and real SAR images to validate the proposed model, specifically focusing on ground-type detection in high dynamic amplitude pixel values scenarios. The performance of the proposed inflated Rayleigh regression model is compared with traditional Gaussian-based regression models, excelling in terms of ground-type detection in a SAR image obtained from the ICEYE radar. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 21, article id 4018705
Keywords [en]
High magnitude pixels, inflated Rayleigh distribution, null pixel values, regression model, SAR images
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-27068DOI: 10.1109/LGRS.2024.3482091ISI: 001346122100008Scopus ID: 2-s2.0-85207727684OAI: oai:DiVA.org:bth-27068DiVA, id: diva2:1912448
Available from: 2024-11-12 Created: 2024-11-12 Last updated: 2024-11-25Bibliographically approved

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Palm, BrunaJavadi, SalehVu, Viet ThuyPettersson, Mats

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