As the amount of data and the complexity of the processing rise, the demand for processing power in remote sensing applications is increasing. The processing speed is a critical aspect to enable a productive interaction between the human operator and the machine in order to achieve ever more complex tasks satisfactorily. Graphic processing units (GPU) are good candidates to speed up some tasks. With the recent developments, programing these devices became very simple. However, one source of complexity is on the frontier of this hardware: how to handle an image that does not have a convenient size as a power of 2, how to handle an image that is too big to fit the GPU memory? This paper presents a framework that has proven to be efficient with standard implementations of image processing algorithms and it is demonstrated that it also enables a rapid development of GPU adaptations. Several cases from the simplest to the more complex are detailed and illustrate speedups of up to 400 times.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.