Parallel Implementation of Polarimetric Synthetic Aperture Radar Data Processing for Unsupervised Classification Using the Complex Wishart Classifier
This work investigates the parallel implementation of target decomposition and unsupervised classification algorithms for polarimetric synthetic aperture radar (POLSAR) data processing. The algorithms are implemented using two different parallel programming models: 1) clusters of CPUs, using message passing interface (MPI), and 2) commodity graphic processing units (GPUs), using the compute device unified architecture (CUDA). POLSAR data processing generally involves a large amount of computations as the full polarimetric information needs to be decomposed and analyzed. Our experiments reveal that GPU architectures provide a good framework for massive parallelization of POLSAR data processing. For instance, it is found that a single GPU can be more efficient than a cluster of 128 nodes with speedups of more than $100 \times $ in comparison with the single processor times. The proposed implementation makes the best use of low-level features in the GPU architecture such as shared memories, while also providing coalesced accesses to memory in order to achieve maximum performance.