Material classification using passive polarimetric imagery

Passive imaging polarimetry has emerged as an useful tool in many remote sensing applications including material classification, target detection and shape extraction. In this paper we present a method to classify specular objects based on their material composition from passive polarimetric imagery. The proposed algorithm is built on an iterative model-based method to recover the complex index of refraction of a specular target from multiple polarization measurements. The recovered parameters are then used to discriminate between objects by employing the nearest neighbor rule. The effectiveness of the proposed method is validated with data collected in laboratory conditions. Experimental results indicate that the classification approach is highly effective for distinguishing between various targets of interest. Most significantly, the proposed classification method is robust to a wide range of observational geometry.