(Automatic) target detection in synthetic aperture radar imagery via terrain recognition

Surveillance of large areas of the Earth´s surface is often undertaken with low resolution synthetic aperture radar (SAR) imagery from either a satellite or a plane. There is a need to process these images with automatic target detection (ATD) algorithms. Typically the targets being searched for are vehicles or small vessels, which occupy only a few resolution cells. Simple thresholding is usually inadequate for detection due to the high amount of noise in the images. Often the background has a discernible texture, and one form of detection is to search for anomalies in the texture caused by the presence of the target pixels. To perform this task a texture model must be able to model a variety of textures at run time, and also model these textures well enough to detect anomalies. We accomplish this with our multiscale nonparametric Markov random field (MRF) texture model