Development of performance metrics to characterize the degree of polarization of man-made objects using passive polarimetric images

Abstract

Spectral sensors are commonly used to measure the intensity of optical radiation and to provide spectral information about the distribution of material components in a given scene, over a limited number of wave bands. By exploiting the polarization of light to measure information about the vector nature of the optical field across a scene, collected polarimetric images have the potential to provide additional information about the shape, shading, roughness, and surface features of targets of interest. The overall performance of target detection algorithms could thus be increased by exploiting these polarimetric signatures to discriminate man-made objects against different natural backgrounds. This is achieved through the use of performance metrics, derived from the computed Stokes parameters, defining the degree of polarization of man-made objects. This paper describes performance metrics that have been developed to optimize the image acquisition of selected polarization angle and degree of linear polarization, by using the Poincare sphere and Stokes vectors from previously acquired images, and then by extracting some specific features from the polarimetric images. Polarimetric signatures of man-made objects have been acquired using a passive polarimetric imaging sensor developed at DRDC Valcartier. The sensor operates concomitantly (bore-sighted images, aligned polarizations) in the visible, shortwave infrared, midwave infrared, and the long-wave infrared bands. Results demonstrate the improvement of using these performance metrics to characterize the degree of polarization of man-made objects using passive polarimetric images.