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10 November 2010 Parallel computing rendering in specific remote sensing image processing
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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.
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Bingjing Mao, Bo Xue, Xiaomei Chen, and Guoqiang Ni "Parallel computing rendering in specific remote sensing image processing", Proc. SPIE 7850, Optoelectronic Imaging and Multimedia Technology, 785028 (10 November 2010);

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