Material Classification of an Unknown Object Using Turbulence-Degraded Polarimetric Imagery

Abstract

In this paper, a material-classification technique using polarimetric imagery degraded by atmospheric turbulence is presented. The classification technique described here determines whether an object is composed of dielectric or metallic materials. The technique implements a modified version of the LeMaster and Cain polarimetric maximum-likelihood blind-deconvolution algorithm in order to remove atmospheric distortion and correctly classify the unknown object. The dielectric/metal classification decision is based on degree-of-linear-polarization (DOLP) maximum-likelihood estimates provided by two novel DOLP priors (one being representative of dielectric materials and the other being representative of metallic materials) developed in this paper. The DOLP estimate, which maximizes the log-likelihood function, determines the image pixel's classification. Included in this paper is the review and modification of the LeMaster and Cain deconvolution algorithm. Also provided is the development of the novel DOLP priors, including their mathematical forms and the physical insight underlying their formulation. Lastly, the experimental results of two dielectric and metallic samples are provided to validate the proposed classification technique.