Sergio Sánchez, Prashanth R. Marpu, Antonio Plaza, Abel Paz-Gallardo

JCR Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, November 2015
vol. 8, no. 11, pp. 1939-5387, ISSN: 1939-1404. DOI: 10.1109/JSTARS.2015.2471083
(Impact factor = 2.145 in 2015)


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× 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.