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Rayleigh Regression Model for Ground Type Detection in SAR Imagery
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Universidade Federal de Santa Maria, BRA.
Universidade Federal de Per nambuco, BRA.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6643-312x
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2019 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 16, no 10, p. 1660-1664Article in journal (Refereed) Published
Abstract [en]

This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 16, no 10, p. 1660-1664
Keywords [en]
Detection, Rayleigh distribution, regression model, reparameterized Rayleigh distribution, synthetic aperture radar (SAR) images
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-17861DOI: 10.1109/LGRS.2018.2881733ISI: 000489756100031OAI: oai:DiVA.org:bth-17861DiVA, id: diva2:1307901
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open access

Available from: 2019-04-29 Created: 2019-04-29 Last updated: 2019-10-31Bibliographically approved

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