A Fast Method For Detecting And Estimating Motion In Radar Images Using Normalized Cross-Correlation

Motion detection and estimation is an important task in several applications of image analysis, including scenarios such as satellite cross-cueing or detecting small shifts in terrain. One widely employed technique for estimating the amount of motion between two images is Normalized Cross-Correlation (NCC), although its computational cost is often prohibitively high for time-sensitive applications. In this work, a previously developed algorithm that uses sum tables to calculate the NCC efficiently for 1-D ultrasound traces is adapted to work for 2-D radar images. The performance of the sum tables algorithm is quantified both theoretically as well as with Synthetic Aperture Radar (SAR) data from the RADARSAT-2 satellite, and is shown to provide time savings of 97% or more compared to the direct method. The algorithm described herein could be used to provide more timely intelligence in situations where it is desirable to detect and estimate the motion of targets using remote sensing.