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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Commodity cluster and hardware-based massively parallel implementations of hyperspectral imaging algorithms
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Commodity cluster and hardware-based massively parallel implementations of hyperspectral imaging algorithms

机译：商品集群和基于硬件的高光谱成像算法的大规模并行实现

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The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several applications exist, however, where having the desired information calculated quickly enough for practical use is highly desirable. High computing performance of algorithm analysis is particularly important in homeland defense and security applications, in which swift decisions often involve detection of (sub-pixel) military targets (including hostile weaponry, camouflage, concealment, and decoys) or chemical/biological agents. In order to speed-up computational performance of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniques. Techniques include four classes of algorithms: (1) unsupervised classification, (2) spectral unmixing, and (3) automatic target recognition, and (4) onboard data compression. A massively parallel Beowulf cluster (Thunderhead) at NASA's Goddard Space Flight Center in Maryland is used to measure parallel performance of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data compression algorithms, a Xilinx Virtex-II field programmable gate array (FPGA) is also used in experiments. Our quantitative and comparative assessment of parallel techniques and strategies may help image analysts in selection of parallel hyperspectral algorithms for specific applications.
机译：机载/卫星平台上集成的高光谱传感器目前正在产生几乎连续的多维图像数据流，而如此高的数据量很快就带来了新的处理挑战。高光谱传感器提供的丰富的空间和光谱信息所付出的代价是它们产生的大量数据。然而，存在几种应用，其中非常需要具有足够快地计算出所需信息以用于实际使用的信息。算法分析的高计算性能在国土防御和安全应用中尤其重要，在快速应用中，快速决策通常涉及检测（亚像素）军事目标（包括敌对武器，伪装，掩盖和诱饵）或化学/生物制剂。为了加快高光谱成像算法的计算性能，本文开发了几种快速并行数据处理技术。技术包括四类算法：（1）无监督分类，（2）频谱分解和（3）自动目标识别以及（4）机载数据压缩。美国宇航局位于马里兰州戈达德太空飞行中心的大规模并行Beowulf集群（雷霆头）用于测量所提出算法的并行性能。为了探索开发机载实时高光谱数据压缩算法的可行性，在实验中还使用了Xilinx Virtex-II现场可编程门阵列（FPGA）。我们对并行技术和策略的定量和比较评估可能会帮助图像分析人员选择针对特定应用的并行高光谱算法。

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