Unsupervised SAR Image Segmentation using Recursive
Partitioning
Vidya Venkatachalam (vidyav@rice.edu) , Robert D. Nowak (nowak@rice.edu) , Richard G. Baraniuk (richb@rice.edu)
Computational Mathematics Laboratory, Rice University
Mario. A. T. Figueiredo
Instituto de Telecomunicacoes, and Instituto Superior Tecnico
We present a new approach
to SAR image segmentation based on a Poisson approximation to the
SAR amplitude image. It has been established that SAR amplitude images are well approximated using Rayleigh distributions.
We show that, with suitable modifications, we can model piecewise homogeneous
regions (such as tanks, roads, scrub, etc.) within the SAR amplitude image
using a Poisson model that bears a known relation to the underlying Rayleigh
distribution. We use the Poisson model to generate an efficient tree-based segmentation
algorithm guided by the
minimum description length (MDL) criteria. We present a simple fixed tree approach, and a more flexible adaptive recursive partitioning scheme. The segmentation is unsupervised, requiring no prior training, and very simple, efficient, and effective for identifying possible regions of interest
(targets).
We present simulation
results on MSTAR clutter data to demonstrate the performance obtained with this parsing technique.