The goal of this SBIR effort is to develop an autonomous capability that image analysts will utilize to extract useful information from a large volume of data in real time. A key component of this capability is the development of a tool that will fully utilize very large intelligence data streams, e.g., those that collect data over spatially wide areas. Areas of interest (unusual activity) will be identified 10 to 100 times faster than analysts can do today. Further, the tool will discover key areas of interest that previously may have escaped identification. Our approach will focus on an innovative Unusual Activity or Inactivity Detector (UAID) using change detection products over wide areas and long periods of time that are commonly obtained from a synthetic aperture radar system. The changes include moving targets, arrival/departure of vehicles, tracks, etc. The UAID will ingest the high-resolution SAR data products and highlight only those areas/activities of high interest to image analysts and decision makers.
Keywords: Unusual Activity Or Inactivity Detector, Change Detection, Gmti, Gaussian Mixing Model, Gotcha 2, Synthetic Aperture Radar
