Proceedings Article | 28 April 2010
Proc. SPIE. 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX
KEYWORDS: Sensors, Image processing, Detection and tracking algorithms, Synthetic aperture radar, Error analysis, Image classification, Image sensors, Image segmentation, Data modeling, Mahalanobis distance
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.
