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

Conference · Sat Aug 01 04:00:00 EDT 1998

OSTI ID:674587

Salazar, J S; Hush, D R; Koch, M W; Fogler, R J; Hostetler, L D

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.

• Research Organization: Sandia National Labs., Albuquerque, NM (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 674587
• Report Number(s): SAND--98-1812C; CONF-981025--; ON: DE98006180
• Country of Publication: United States
• Language: English

Related Subjects

44 INSTRUMENTATION
INCLUDING NUCLEAR AND PARTICLE DETECTORS
DATA ANALYSIS
MATHEMATICAL MODELS
MAXIMUM-LIKELIHOOD FIT
PATTERN RECOGNITION
RADAR
STATISTICS