Robust detection of object boundaries in Weibull radar imagery

TLDR: The efficacies of edge detectors defined as ratios of single order statistics, ratios of averages and ratios of best linear unbiased estimators (BLUEs) are compared and are based on computed error probabilities as the Weibull parameters are varied.

Abstract: Radar image speckle is often modeled as having a negative-exponential, or more generally, gamma distribution. However, studies of noise in coherent radar systems suggest that the first-order statistics may be more accurately modeled using the two-parameter Weibull density, the parameters of which vary with the surface being imaged. Techniques for detecting object boundaries in noisy radar images are proposed and compared. The images are assumed to be coarsely sampled, so that the (multiplicative) radar noise can be modeled as uncorrelated and identically distributed. Edge detection in multiplicative noise is effectively accomplished by thresholding ratios of locally adjacent image estimates. The efficacies of edge detectors defined as ratios of single order statistics, ratios of averages and ratios of best linear unbiased estimators (BLUEs) are compared. The comparisons are based on computed error probabilities as the Weibull parameters are varied. Several example images are provided for empirical comparison as well. >