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Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection
Federal University of Santa Maria, BRA.ORCID iD: 0000-0003-0626-0316
Saab Surveillance AB, SWE.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6643-312x
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0003-3945-8951
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2019 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 16, p. 756-760Article in journal (Refereed) Published
Abstract [en]

The aim of this letter is to compare two incoherent change-detection algorithms for target detection in low-frequency ultrawideband (UWB) synthetic aperture radar (SAR) images. The considered UWB SAR operates in the frequency range from 20 to 90 MHz. Both approaches employ a likelihood ratio test according to the Neyman–Pearson criterion. First, the bivariate Rayleigh probability distribution is used to implement the likelihood ratio test function. This distribution is well known and has been used for change-detection algorithms in low-frequency UWB SAR with good results. Aiming to minimize the false alarm rate and taking into consideration that low-frequency UWB SAR images have high resolution compared to the transmitted wavelength, the second approach implements the test by using a bivariate K-distribution. This distribution has scale and shape parameters that can be used to adjust it to the data. No filter is applied to the data set images, and the results show that with a good statistical model, it is not needed to rely on filtering the data to decrease the number of false alarms. Therefore, we can have a better tradeoff between resolution and detection performance.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 16, p. 756-760
Keywords [en]
Change detection, likelihood ratio test, synthetic aperture radar (SAR).
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-17863DOI: 10.1109/LGRS.2018.2881733ISI: 000466228400019OAI: oai:DiVA.org:bth-17863DiVA, id: diva2:1307981
Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2019-06-14Bibliographically approved

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Pettersson, MatsVu, Viet Thuy

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