Performance complexity study of several approaches to automatic target recognition from SAR images

doi:10.1109/taes.2002.1008992

Cited by 35 publications

References 24 publications

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Automatic target recognition

Mishra

Mulgrew

2010

Encyclopedia of Aerospace Engineering

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One of the important forms of military intelligence consists of recognizing the type of target in the battle field. If good‐quality optical image of the battle scene is available, target recognition is done mostly by human experts. However, for sensors like infrared sensors and radars, target recognition solely by human experts is difficult. Hence, application of pattern recognition techniques, mainly in systems based on sensors like these, is prevalent and is known as automatic target recognition (ATR). This chapter will start with a short description of ATR and the challenges involved. Radar‐based ATR, especially synthetic aperture radar (SAR)‐based ATR, is one of the most popular ATR schemes, and hence the rest of the chapter will be devoted to SAR ATR. We will first describe the different stages of a generic ATR system and then elaborate some of the major challenges of radar‐based ATR. This will be followed by a description about the major figures of merit for a SAR ATR algorithm. The chapter will discuss some of the important algorithms from the open literature dealing with radar‐based ATR. We will also discuss some of the recent developments in the field of ATR, for example, bistatic radar‐based ATR, polarimetric radar‐based ATR, and multi‐look radar‐based ATR. The chapter will close with a note on the futuristic radar‐based ATR systems.

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Automatic target recognition

Mishra

Mulgrew

2010

Encyclopedia of Aerospace Engineering

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Clutter reduction in synthetic aperture radar images with statistical modeling: An application to MSTAR data

Demirci

Özdemir

Akdağlı

et al. 2008

Micro & Optical Tech Letters

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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

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SVM-based target recognition from synthetic aperture radar images using target region outline descriptors

Anagnostopoulos

2009

Nonlinear Analysis: Theory, Methods & Applications

116

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Performance complexity study of several approaches to automatic target recognition from SAR images

Cited by 35 publications

References 24 publications

Automatic target recognition

Automatic target recognition

Clutter reduction in synthetic aperture radar images with statistical modeling: An application to MSTAR data

SVM-based target recognition from synthetic aperture radar images using target region outline descriptors

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