Statistical modeling of targets and clutter in single-look non-polarimetric SAR imagery

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This paper presents a Generalized Logistic (gLG) distribution as a unified model for Log-domain synthetic aperture Radar (SAR) data. This model stems from a special case of the G-distribution known as the G{sup 0}-distribution. The G-distribution arises from a multiplicative SAR model and has the classical K-distribution as another special case. The G{sup 0}-distribution, however, can model extremely heterogeneous clutter regions that the k-distribution cannot model. This flexibility is preserved in the unified gLG model, which is capable of modeling non-polarimetric SAR returns from clutter as well as man-made objects. Histograms of these two types of SAR returns have opposite ... continued below

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5 p.

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Salazar, J.S.; Hush, D.R.; Koch, M.W.; Fogler, R.J. & Hostetler, L.D. August 1, 1998.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

This paper presents a Generalized Logistic (gLG) distribution as a unified model for Log-domain synthetic aperture Radar (SAR) data. This model stems from a special case of the G-distribution known as the G{sup 0}-distribution. The G-distribution arises from a multiplicative SAR model and has the classical K-distribution as another special case. The G{sup 0}-distribution, however, can model extremely heterogeneous clutter regions that the k-distribution cannot model. This flexibility is preserved in the unified gLG model, which is capable of modeling non-polarimetric SAR returns from clutter as well as man-made objects. Histograms of these two types of SAR returns have opposite skewness. The flexibility of the gLG model lies in its shape and shift parameters. The shape parameter describes the differing skewness between target and clutter data while the shift parameter compensates for movements in the mean as the shape parameter changes. A Maximum Likelihood (ML) estimate of the shape parameter gives an optimal measure of the skewness of the SAR data. This measure provides a basis for an optimal target detection algorithm.

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5 p.

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OSTI as DE98006180

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  • IASTED international conference: signal and image processing, Las Vegas, NV (United States), 27-31 Oct 1998

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  • Other: DE98006180
  • Report No.: SAND--98-1812C
  • Report No.: CONF-981025--
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 674587
  • Archival Resource Key: ark:/67531/metadc702513

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  • August 1, 1998

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  • Sept. 12, 2015, 6:31 a.m.

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  • April 14, 2016, 8:09 p.m.

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Salazar, J.S.; Hush, D.R.; Koch, M.W.; Fogler, R.J. & Hostetler, L.D. Statistical modeling of targets and clutter in single-look non-polarimetric SAR imagery, article, August 1, 1998; Albuquerque, New Mexico. (https://digital.library.unt.edu/ark:/67531/metadc702513/: accessed April 20, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.