Application of a maximum likelihood algorithm to the detection of targets in synthetic aperture radar (SAR) images

A detection algorithm was developed using the Maximum Likelihood Adaptive Neural System a neural network that adaptively estimates the probability density functions (pdf) of all classes of objects in the data set. This algorithms was used to detect downed aircraft in a heavy clutter environment in SAR images. In this study the portion of the image under study contains hundreds of thousands of pixels, the pixel statistics are estimated and the pixel having the lowest likelihood is labeled as the target pixel. This is an unsupervised learning approach to the target detection problem because no learning data on the background or the target is used to detect the target. The approach relies on an accurate estimate of the image likelihood function in order to provide a good characterization of the scene. This approach was applied to several images collected in a variety of heavily wooded areas under different environmental conditions with excellent results. This approach was also used to provide insight into the scene phenomenology by associating specific basis functions in each likelihood characterization with particular attributes of the image.