Rapid location of radiation sources in complex environments using optical and radiation sensors

Baseline radiation background is almost never known and constantly changes particularly in urban areas. It is difficult to know what the expected background radiation should be and how a radiological incident may elevate the radiation. Naturally occurring radiation from rocks and building materials often contributes significantly to measured radiation. Buildings and other tall structures also shield radiation and thus need to be taken into account. Models of natural occurring background radiation can be derived from knowledge of geology, building material origins, vegetation, and weather conditions. After a radiological incident, the radiation will be elevated near the event, and some material may be transported by mechanisms such as airborne transport and/or run-off. Locating and characterizing the sources of radiation quickly and efficiently are crucial in the immediate aftermath of a nuclear incident. The distribution of radiation sources will change naturally and also due to clean-up efforts. Finding source strengths and locations during both the initial and clean-up stages is necessary to manage and reduce contaminations. The overall objective of the Rapid Location Of Radiation Sources In Complex Environments Using Optical And Radiation research project is to design and validate gamma ray spectrum estimation algorithms that integrate optical and radiation sensor collections into high resolution, multi-modal site models for use in radiative transport codes. Our initial focus is on modeling the background radiation using hyper-spectral information from visible through the shortwave infrared sensors and thermal imagers. The optical data complements available ancillary data from other sources such as Geographic Information Systems (GIS) layers, e.g. geologic maps, terrain, surface cover type, road network, vegetation (e.g. serpentine vegetation), 3-D building models, known users of radiological sources, etc. In absence of GIS layers, the data from the multi/hyper-spectral imager and height data from LIDAR can be analyzed with special with special software to automatically create GIS layers and radiation survey data to come up with a method to predict background radiation distribution. We believe the estimation and prediction of the natural background will be helpful in finding anomalous point, line and small area sources and minimize the number of false alarms due to natural and known man-made radiation sources such as radiological medical facilities, industrial users of radiological sources.