The problem of target classification using synthetic aperture radar (SAR) polarizations is considered form a Bayesian decision point of view. This problem is analogous to the multi-sensor problem. We investigate the optimum design of a data fusion structure given that each classifier makes a target classification decision for each polarimetric channel. Thought the optimal structure is difficult to implement without complete statistical information, we show that significant performance gains can be made even without a perfect model. First, we analyze the problem from an optimal classification point of view using a simple classification problem by outlining the relationship between classification and fusion. Then, we demonstrate the performance improvement by fusing the decisions from a Gram Schmidt image classifier for each polarization.

Date Published: 10 June 1996
PDF: 9 pages
Proc. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, (10 June 1996); doi: 10.1117/12.242054

Published in SPIE Proceedings Vol. 2757:
Algorithms for Synthetic Aperture Radar Imagery III
Edmund G. Zelnio; Robert J. Douglass, Editor(s)