The use of GPUs in image processing

The analysis of climatic parameters, vegetation, humidity and pollution in the domain of time and space is done by processing a series of images of a geographic area taken by the satellite at certain times [1]. These images are subject to several computing schemes, with the aim of evaluating spatial and temporal variations of the mentioned parameters. One of the programs used to manipulate the images is the CHERS, which is completed in the framework of the FP7 project SEE-GRID-SCI [2]. This program calculates polynomial trend in time for pixels of ordered sets of images. In this paper we have considered the parallelization of the CHERS algorithm. The parallelization technique implemented in this study is Cuda, which is used to program multicore NVIDIA GPUs. It is observed a decrease in user and system time proportional to the number of active threads. Also, CPU percentage falls to a minimum of 69%.