CSDL Home C CVPRW 2008 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
Firooz Sadjadi , Lockheed Martin Corporation, Saint Anthony, Minnesota, USA
Anders Sullivan , Army Research Laboratory, Adelphi, Maryland, USA
Guillermo Gaunaurd , Army Research Laboratory, Adelphi, Maryland, USA
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 basis 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) exploiting critical attributes by computing changes in focused sub-spaces. In this paper we present anew approach for polarimetric change detection, whereby the target is represented not in terms of the complex scattering elements but in terms of phenomenologically-based Huynen parameters. Each element of the Huynen parameter set conveys useful physical and geometrical attributes about the scatterers thus augmenting the potential for significant false alarm mitigation. Results of the application of this approach on fully polarimetric signatures of simulated buried targets are provided. These results indicate that Huynen parameters are more effective for change detection than the scattering matrix elements in generating higher unambiguous autocorrelation peaks and less prominent cross-correlation peaks.
Firooz Sadjadi, Anders Sullivan, Guillermo Gaunaurd, "Detection of buried objects using GPR change detection in polarimetric Huynen spaces", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-6, doi:10.1109/CVPRW.2008.4563063