Signal Processing Commons^™

Binary Classification Of An Unknown Object Through Atmospheric Turbulence Using A Polarimetric Blind-Deconvolution Algorithm Augmented With Adaptive Degree Of Linear Polarization Priors, Mu J. Kim

Theses and Dissertations

This research develops an enhanced material-classification algorithm to discriminate between metals and dielectrics using passive polarimetric imagery degraded by atmospheric turbulence. To improve the performance of the existing technique for near-normal collection geometries, the proposed algorithm adaptively updates the degree of linear polarization (DoLP) priors as more information becomes available about the scene. Three adaptive approaches are presented. The higher-order super-Gaussian method fits the distribution of DoLP estimates with a sum of two super-Gaussian functions to update the priors. The Gaussian method computes the classification threshold value, from which the priors are updated, by fitting the distribution of DoLP estimates …