基于GPU的高通滤波融合

Jun Lu, Baoming Zhang, Hong He, Hongwei Zhang
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引用次数: 7

摘要

随着现代遥感技术的发展,获取的遥感图像数据越来越丰富,因此未来遥感技术应用的主要障碍不再是图像资源的不足,而是如何从图像资源中获取更丰富、更有用、更可信的信息的能力。多传感器遥感图像融合是地面观测信息获取的重要组成部分,也是解决海量遥感图像数据问题的重要途径。如果算法能被普遍使用,处理速度就成为算法的关键。本文利用并行矢量处理器GPU的可编程性,设计了一种基于GPU的遥感图像数据高通滤波融合算法。结果表明,在大数据情况下,该算法在GPU上的运行速度要比基于cpu的算法快得多。随着融合图像数据量的增大,在GPU上的速度优势比在CPU上的优势更明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The High-Pass Filtering Fusion Based on GPU
Along with the development of the modern remote sensing technology, the acquired remote sensing image data gets more and more abundant, so the primary obstruction of the application of the remote sensing technology in the future is no longer the shortage of the image resource, but the capacity that how we can get more abundant, more useful and more credible information from the image resource. Multi-sensors remote sensing image fusion is an important apart of the information acquisition of the ground observation and also an important approach to resolve the problem of the mass remote sensing image data. The processing speed becomes a key point of an algorithm if can be in general use. In this paper we designed a high-pass filtering fusion algorithm of remote sensing image data in GPU (Graphics Processing Unit) using the programmability of GPU, which is a parallel vector processor. The result shows that the algorithm runs on a GPU is much faster than the CPU-based algorithm in the case of large data. And with the volumes of fusion images data getting bigger the advantage of the velocity on GPU is more obvious then on CPU.
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