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27 April 2010 IDC: a system for automatically detecting and classifying manmade objects in overhead imagery
Mark J. Carlotto, Mark Nebrich, David De Michael
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Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 769712 (2010) https://doi.org/10.1117/12.848527
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States
Abstract
The automatic detection and classification of manmade objects in overhead imagery is key to generating geospatial intelligence (GEOINT) from today's high space-time bandwidth sensors in a timely manner. A flexible multi-stage object detection and classification capability known as the IMINT Data Conditioner (IDC) has been developed that can exploit different kinds of imagery using a mission-specific processing chain. A front-end data reader/tiler converts standard imagery products into a set of tiles for processing, which facilitates parallel processing on multiprocessor/multithreaded systems. The first stage of processing contains a suite of object detectors designed to exploit different sensor modalities that locate and chip out candidate object regions. The second processing stage segments object regions, estimates their length, width, and pose, and determines their geographic location. The third stage classifies detections into one of K predetermined object classes (specified in a models file) plus clutter. Detections are scored based on their salience, size/shape, and spatial-spectral properties. Detection reports can be output in a number of popular formats including flat files, HTML web pages, and KML files for display in Google Maps or Google Earth. Several examples illustrating the operation and performance of the IDC on Quickbird, GeoEye, and DCS SAR imagery are presented.
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Mark J. Carlotto, Mark Nebrich, and David De Michael "IDC: a system for automatically detecting and classifying manmade objects in overhead imagery", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 769712 (27 April 2010); https://doi.org/10.1117/12.848527
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KEYWORDS
Sensors
Image processing
Detection and tracking algorithms
Synthetic aperture radar
Error analysis
Image classification
Image sensors
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