Stream Model-Based Orthorectification in a GPU Cluster Environment

Abstract

One of the most important tasks in remote sensing data processing is the production of orthorectified images. Such tasks are computationally intensive and can become a bottleneck for remote sensing image processing, particularly in high-throughput environments, such as large satellite imagery processing centers. This letter explores the use of massive parallel processing graphical processing unit (GPU) in a clustered network environment to speed up image processing tasks, such as orthorectification. Our parallelization method is based on inverse sensor model and the stream model for image processing, which allow the flexibility of placing computational units on proper computation units, such as GPU, CPU cores, or nodes in a cluster. In our experiments on images of two satellites, more than 198 times and 50.3 times speedup over one and multiple thread CPU versions have been achieved, respectively.