Unsupervised SAR Image Segmentation using Recursive Partitioning

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 modi cations 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 e cient tree based segmentation algorithm guided by the minimum description length MDL criteria We present a simple xed tree approach and a more exible adaptive recursive partitioning scheme The segmentation is unsupervised requiring no prior training and very simple e cient and e ective for identifying possible regions of interest targets We present simulation results on MSTAR clutter data to demonstrate the performance obtained with this parsing technique