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Clutter reduction in synthetic aperture radar images with statistical modeling: An application to MSTAR data
Author(s) -
Demirci Sevket,
Ozdemir Caner,
Akdagli Ali,
Yigit Enes
Publication year - 2008
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.23413
Subject(s) - clutter , constant false alarm rate , weibull distribution , synthetic aperture radar , computer science , artificial intelligence , radar , statistical model , moving target indication , pattern recognition (psychology) , remote sensing , computer vision , radar imaging , mathematics , statistics , continuous wave radar , geology , telecommunications
In this article, an application of clutter modeling and reduction techniques to synthetic aperture radar (SAR) images of moving and stationary target acquisition and recognition data is presented. Statistical modeling of the clutter signal within these particular SAR images is demonstrated. Lognormal, Weibull, and K‐distribution models are analyzed for the amplitude distribution of high‐resolution land clutter data. Higher‐order statistics (moments and cumulants) are utilized to estimate the appropriate statistical distribution models for the clutter. Also, Kolmogorov‐Smirnov (K‐S) goodness‐of‐fit test is employed to validate the accuracy of the selected models. With the use of the determined clutter model, constant false‐alarm rate detection algorithm is applied to the SAR images of several military targets. Resultant SAR images obtained by using the proposed method show that target signatures are reliably differentiated from the clutter background. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1514–1520, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23413