Advanced algorithms and high-performance testbed for large-scale site characterization and subsurface target detection using airborne ground-penetrating SAR

A team of US Army Corps of Engineers, Omaha District and Engineering and Support Center, Huntsville, JPL, Stanford Research Institute (SRI), and Montgomery Watson is currently in the process of planning and conducting the largest ever survey at the Former Buckley Field, in Colorado, by using SRI airborne, ground penetrating, SAR. The purpose of this survey is the detection of surface and subsurface Unexploded Ordnance (UXO) and in a broader sense the site characterization for identification of contaminated as well as clear areas. In preparation for such a large-scale survey, JPL has been developing advanced algorithms and a high-performance testbed for processing of massive amount of expected SAR data from this site. Two key requirements of this project are the accuracy and speed of SAR data processing. The first key feature of this testbed is a large degree of automation and maximum degree of the need for human perception in the processing to achieve an acceptable processing rate of several hundred acres per day. For accuracy UXO detection, novel algorithms have been developed and implemented. These algorithms analyze dual polarized SAR data. They are based on the correlation of HH and VV SAR data and involve a rather large set of parameters for accurate detection of UXO. For each specific site, this set of parameters can be optimized by using ground truth data. In this paper, we discuss these algorithms and their successful application for detection of surface and subsurface anti-tank mines by using a data set from Yuma Proving Ground, AZ, acquired by SRI SAR.