Date of Award
Master of Science
Department of Electrical and Computer Engineering
Milo W. Hyde, PhD.
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 with a sum of two Gaussian functions. Lastly, the distribution-averaging method approximates the threshold value by finding the mean of the DoLP distribution. The experimental results confirm that the new adaptive method significantly extends the collection geometry range of validity for the existing technique.
DTIC Accession Number
Kim, Mu J., "Binary Classification of an Unknown Object through Atmospheric Turbulence Using a Polarimetric Blind-Deconvolution Algorithm Augmented with Adaptive Degree of Linear Polarization Priors" (2012). Theses and Dissertations. 1126.