Point target detection using super-resolution reconstruction

Abstract

Surveillance applications are primarily concerned with detection of targets. In electro-optical surveillance systems, missiles or other weapons coming towards you are observed as moving points. Typically, such moving targets need to be detected in a very short time. One of the problems is that the targets will have a low signal-to-noise ratio with respect to the background, and that the background can be severely cluttered like in an air-to-ground scenario. The first step in detection of point targets is to suppress the background. The novelty of this work is that a super-resolution reconstruction algorithm is used in the background suppression step. It is well-known that super-resolution reconstruction reduces the aliasing in the image. This anti-aliasing is used to model the specific aliasing contribution in the camera image, which results in a better estimate of the clutter in the background. Using super-resolution reconstruction also reduces the temporal noise, thus providing a better signal-to-noise ratio than the camera images. After the background suppression step common detection algorithms such as thresholding or track-before-detect can be used. Experimental results are given which show that the use of super-resolution reconstruction significantly increases the sensitivity of the point target detection. The detection of the point targets is increased by the noise reduction property of the super-resolution reconstruction algorithm. The background suppression is improved by the anti-aliasing.