Model-based estimation of surface geometry using passive polarimetric imaging

Abstract

Imaging polarimetry has emerged as a powerful tool for application in the field of remote sensing. In this paper, we present a novel technique for estimating the surface normal angle of each of the individual facets of a target object using passive polarimetric data. The passive polarimetric imaging system described here uses multiple measurements of the output Stokes vectors along with the reflection Mueller matrix, to extract the surface normal angle corresponding to individual facets of the target object. The knowledge of this parameter is indispensable for determining the orientation and surface geometry of the target object and thus facilitates applications like object recognition, shape extraction and building scene geometry. The worst-case error is found to be less than 2%, based on Monte Carlo computer simulation results.