UNCERTAINTIES IN THE ESTIMATION OF ACF-BASED TEXTURE IN SYNTHETIC-APERTURE RADAR IMAGE DATA

Spatial analysis of synthetic aperture radar (SctR) image data holds m uch promise for characterizing and discriminating environmental scene elements. The autocorrelation function (ACF) has been identified as a potentially useful spatial metric because it admits an analysis with c onventional linear system theory, Recent models of spatial scattering suggest that ACF-based texture analysis of SAR image data is capable o f discriminating between a variety of area extensive targets, The inco rporation of texture in an image classification or segmentation system requires same understanding of the uncertainties in the texture estim ates. In this paper, we introduce a particular ACF model and examine t he errors associated with estimating its parameters from image measure ments. We also conduct an analysis of two important classes of errors: imaging system errors and estimation errors. We found that as the pro portion of raw signal used to create the image increases the effects o f system errors rapidly degrade ACF performance. This has implications for operationally produced image products that do not use an autofocu sing procedure. We also found that the agreement between theoretical a nd observed estimation errors is quite good, so that the scale of thes e errors may be accurately estimated during a spatial analysis of the image data. We found some residual bias that may be attributed to both the use of the ACF itself and to the way the ACF model was constructe d.