Enhanced material classification using turbulence-degraded polarimetric imagery

An enhanced material-classification algorithm using turbulence-degraded polarimetric imagery is presented. The proposed technique improves upon an existing dielectric/metal material-classification algorithm by providing a more detailed object classification. This is accomplished by redesigning the degree-of-linear-polarization priors in the blind-deconvolution algorithm to include two subclasses of metals--an aluminum group classification (includes aluminum, copper, gold, and silver) and an iron group classification (includes iron, titanium, nickel, and chromium). This new classification provides functional information about the object that is not provided by existing dielectric/metal material classifiers. A discussion of the design of these new degree-of-linear-polarization priors is provided. Experimental results of two painted metal samples are also provided to verify the algorithm's accuracy.