Point target detection using super-resolution reconstruction

Point target detection using super-resolution reconstruction

TNO Defensie en Veiligheid
de Lange, D.J.J.
Dijk, J.
van Eekeren, A.W.M.
Schutte, K.

Sadjadi, F.A. (editor)

2007

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.

Super-resolution reconstruction
Target detection
Point target
Background

http://resolver.tudelft.nl/uuid:a5c995c8-1e1d-4b8c-9ca7-80eae5c11d3c

219140

SPIE, Bellingham, WA

Automatic Target Recognition XVII, 9 April 2007, Orlando, FL, USA

Proceedings of SPIE

conference paper