Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem
that human analysts try to solve by applying world knowledge. Often there are known observables that can
be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each
of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary
observables may provide sufficient context for detection by a machine learning algorithm.
We describe an approach for automatic detection of facilities that uses an automated feature extraction
algorithm to extract auxiliary observables, and a semi-supervised assisted target recognition algorithm to then
identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image
data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature
extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as
parking lots, large buildings, sports fields and residential areas and then combine these features using Genie
Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.