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Feature phenomenology and feature extraction of civilian vehicles from SAR images

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.