Because of the more in-depth scientific research, remote sensing images often contain huge amounts of information.
Therefore, remote sensing images always have features with multi-dimensions details and huge size. In order to obtain
the ground information more accurately from the images, the remote sensing image processing would have several steps
in the aim of better image restore and the image information refining.
Frequently, processing for this type of images has faced to some difficult issues, such as calculating slowly or consuming
huge in resources. For this reason, the parallel computing rendering in remote sensing image processing is essentially
necessary. The parallel computing method approached in this paper does not require the original algorithm rewriting.
Under a distributed framework, the method allocated the original algorithm efficiently to the multiple computing cores of
the processing computer. Because this method has fully use the computing resources, so the calculating time would be
reduced linearly with the number of computing threads. What's more, the method can also truly guarantee the integrity
of the remote sensing image data.
For the purpose of validating the feasibility of the method, this paper put the parallel computing method on application,
in which the method rendering into a radiation simulation of remote sensing image processing. We conducted several
experiments and got the statistical results. We integrated the parallel computing into the core of the original algorithm -
the wide huge size convolution. The experimental results showed that the computing efficiency improved linearly. The
number of computer calculating core was proportionally related to the reduced rate of computing time. At the same time,
the computing results were identical to the original results.