Adaptive binary material classification of an unknown object using polarimetric images degraded by atmospheric turbulence

Abstract

An improved binary material-classification algorithm using passive polarimetric imagery degraded by atmospheric turbulence is presented. The technique implements a modified version of an existing polarimetric blind-deconvolution algorithm in order to remove atmospheric distortion and correctly classify the unknown object. The classification decision, dielectric or metal in this case, is based on degree of linear polarization (DoLP) estimates provided by the blind-deconvolution algorithm augmented by two DoLP priors – one statistically modeling the polarization behavior of metals and the other statistically modeling the polarization behavior of dielectrics. The DoLP estimate which maximizes the log-likelihood function determines the image pixel's classification. The method presented here significantly improves upon a similar published polarimetric classification method by adaptively updating the DoLP priors as more information becomes available about the scene. This new adaptive method significantly extends the range of validity of the existing polarimetric classification technique to near-normal collection geometries where most polarimetric material classifiers perform poorly. In this paper, brief reviews of the polarimetric blind-deconvolution algorithm and the functional forms of the DoLP priors are provided. Also provided is the methodology for making the algorithm adaptive including three techniques for updating the DoLP priors using in-progress DoLP estimates. Lastly, the proposed technique is experimentally validated by comparing classification results of two dielectric and metallic samples obtained using the new method to those obtained using the existing technique.