On the use of cluster computing architectures for implementation of hyperspectral image analysis algorithms
On the use of cluster computing architectures for implementation of hyperspectral image analysis algorithms
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利用集群计算架构实现高光谱图像分析算法
DOI：
10.1109/iscc.2005.114
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
J. Plaza
中科院分区：
文献类型：
--
作者：
D. Valencia;A. Plaza;P. Martínez;J. Plaza
高光谱传感器是目前可用于地球遥感的最先进的仪器。由NASA喷气推进实验室开发的机载可见光红外成像光谱仪(AVIRIS)等系统提供的图像具有高空间和光谱分辨率，使其能够用于各种应用，如探测和控制野火和水和大气中的危险物质、探测军事目标和管理自然资源。尽管上述应用程序需要实时响应，但几乎没有可用的解决方案来提供对这些类型数据的快速高效分析。这主要是由于高光谱图像的维度限制了其在空间和时间要求非常高的分析场景中的应用。在目前的工作中，我们描述了一种新的并行方法，它处理了以前解决的大多数问题。使用两个并行计算机系统--位于巴塞罗那欧洲并行中心的SGI Origin 2000共享存储系统和位于NASA戈达德太空飞行中心的Thunderhead Beowulf星系团--对所提出的分析方法的计算性能进行了评估。
Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the airborne visible infra-red imaging spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wild fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications require a response in real time, few solutions are available to provide fast and efficient analysis of these types of data. This is mainly caused by the dimensionality of hyperspectral images, which limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In the present work, we describe a new parallel methodology which deals with most of the previously addressed problems. The computational performance of the proposed analysis methodology is evaluated using two parallel computer systems, a SGI Origin 2000 shared memory system located at the European Center of Parallelism of Barcelona, and the Thunderhead Beowulf cluster at NASA's Goddard Space Flight Center.