Abstract
AbstractIn 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
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
