Detection of facilities in satellite imagery using semi-supervized image classification and auxiliary contextual observables

Neal R. Harvey; C. Ruggiero; N. H. Pawley; B. MacDonald; A. Oyer; L. Balick; S. P. Brumby

doi:10.1117/12.819330

27 April 2009 Detection of facilities in satellite imagery using semi-supervized image classification and auxiliary contextual observables

Neal R. Harvey, C. Ruggiero, N. H. Pawley, B. MacDonald, A. Oyer, L. Balick, S. P. Brumby

Author Affiliations +

Proceedings Volume 7341, Visual Information Processing XVIII; 73410Q (2009) https://doi.org/10.1117/12.819330
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

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.

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Neal R. Harvey, C. Ruggiero, N. H. Pawley, B. MacDonald, A. Oyer, L. Balick, and S. P. Brumby "Detection of facilities in satellite imagery using semi-supervized image classification and auxiliary contextual observables", Proc. SPIE 7341, Visual Information Processing XVIII, 73410Q (27 April 2009); https://doi.org/10.1117/12.819330

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