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Proceedings Paper

Feature phenomenology and feature extraction of civilian vehicles from SAR images
Author(s): Christopher Paulson; Dapeng Wu
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Paper Abstract

Being able to recognize one object from another is vital research to our society because it can save lives, improve national security, and improve existing technology such as object avoidance, tracking, etc. In this research we are trying to classify Synthetic Aperture Radar (SAR) images of vehicles from one another no matter if the vehicle is rotated or occluded. The dataset that is being used for this research is the Commercial Vehicle (CV) Data Domes obtained fromWright Patterson Air Force Base (WPAFB). To accomplish this task we used Local Feature Extraction (LFE) to extract the features and then K-nearest neighbor (KNN) was used to classify the vehicles. Overall this method performed well in that the algorithm was able to correctly identify the vehicles 97.6% to 100% accuracy. Currently the algorithm can not handle translation, so the next step of this research is to be able to use the glint information to register the vehicles to a desired location and then perform our algorithm which we believe that registering the image would have a significant improvement to the current results.

Paper Details

Date Published: 20 May 2011
PDF: 38 pages
Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 80510X (20 May 2011); doi: 10.1117/12.887594
Show Author Affiliations
Christopher Paulson, Univ. of Florida (United States)
Dapeng Wu, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 8051:
Algorithms for Synthetic Aperture Radar Imagery XVIII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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