Spatial scan density estimates

The detection and identification of regions of interest in spatial or tempo ral data is a common concern in automatic target recognition. One approach to region-of-interest identification involves the use of spatial scan stati stics. A difficulty arises due to competing concerns: Small scan windows ar e required for potentially small targets, but larger scan windows are neces sary to improve the accuracy of the detector. When the scan statistics are mixture-model density estimates, a borrowed strength profile likelihood app roach is shown to be superior to conventional likelihood estimators. We inv estigate these spatial scan density estimates on example imagery from an un manned aerial vehicle.