Detection of Buried Objects using GPR Change Detection in Polarimetric Huynen Spaces Firooz Sadjadi Lockheed Martin Corporation Saint Anthony, Minnesota USA firooz.sadjadi@ieee.org Anders Sullivan Army Research Laboratory Adelphi, Maryland, USA Guillermo Gaunaurd Army Research Laboratory Adelphi, Maryland, USA Abstract Change detection is a useful method for detecting new events in a scene such as the placement of mines, and/or the movement of people, vehicles and structures. The ba- sis of the approach is to examine an area via radar several times. Once, before there were targets planted there, and the other (or others) after. The change detection algorithm will notice if there are any changes after the first view was made. False alarm, that is a critical issue in this approach, can be reduced in a number of ways: exploiting additional information such as phase and polarization, and 2) exploit- ing critical attributes by computing changes in focused sub- spaces. In this paper we present a new approach for polari- metric change detection, whereby the target is represented not in terms of the complex scattering elements but in terms of phenomenologically-based Huynen parameters. Each el- ement of the Huynen parameter set conveys useful physical and geometrical attributes about the scatterers thus aug- menting the potential for significant false alarm mitigation. Results of the application of this approach on fully polari- metric signatures of simulated buried targets are provided. These results indicate that Huynen parameters are more ef- fective for change detection than the scattering matrix el- ements in generating higher unambiguous autocorrelation peaks and less prominent cross-correlation peaks. 1. Introduction Change detection (CD) has been shown to be a useful method for detecting new events in a scene such as the placement of mines, and/or the movement of people, vehi- cles and structures. The basis of the approach is to examine an area via usually radar several times. Once, before there were targets planted there, and the other (or others) after. The change detection algorithm will notice if there are any changes after the first view was made. If there are, one then has to determine the rate of false alarms to insure that the change is due to a real threat. Since it is almost certain that there will be changes noticeable, the key to the approach is to reduce / eliminate the false alarms. To reduce false alarms and improve detection probabilities, the process of change detection in radar that includes both imaging and non-imaging systems, has gone through a number of evolu- tionary stages: 1) Noncoherent change detection, whereby only the radar cross sections are used in the change estima- tion; this approach has very limited use. 2) Coherent non- polarimetric change detection [1], whereby both the phase and amplitude of a single polarization scattering matrix are used for change estimation. This approach is the most common, but suffers from high numbers of false alarms. 3) Polarimetric vector coherent change detection, whereby three complex scattering elements for vertical, horizontal and cross polarizations are used for the change detection. In this paper we present a new approach for polari- metric change detection using ground penetrating radar (GPR) signatures, whereby the target is represented not in terms of three complex scattering elements but in terms of phenomenologically-based Huynen parameters. Each ele- ment of the Huynen parameter set conveys useful physi- cal and geometrical attributes about the scatterers thus aug- menting the potential for significant false alarm mitigation. Results of the application of this approach on fully polari- metric signatures of simulated buried targets are provided. These results indicate that Huynen parameters are more ef- fective for change detection than the scattering matrix el- ements in generating higher unambiguous autocorrelation peaks and more non-dominating cross-correlation curves. 2. Stokes and Huynen-fork parameters rela- tionships From a Sinclair matrix S [2], the product S*S has eigen- values obtained from the eigenvalue equation: |S ∗ S -|γ | 2 I | =0 (1) 978-1-4244-2340-8/08/$25.00 ©2008 IEEE