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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Parallel implementation of endmember extraction algorithms from hyperspectral data
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Parallel implementation of endmember extraction algorithms from hyperspectral data

机译：从高光谱数据并行提取末端成员算法

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摘要

Automated extraction of spectral endmembers is a crucial task in hyperspectral data analysis. In most cases, the computational complexity of endmember extraction algorithms is very high, in particular, for very high-dimensional datasets. However, the intrinsic properties of available techniques are amenable to the design of parallel implementations. In this letter, we evaluate several parallel algorithms that represent three representative approaches to the problem of extracting endmembers. Two parallel algorithms have been selected to represent a first class of algorithms based on convex geometry concepts. In particular, we develop parallel implementations of approximate versions of the N-FINDR and pixel purity index algorithms, along with a parallel hybrid of both techniques. A second class is given by algorithms based on constrained error minimization and represented by a parallel version of the iterative error analysis algorithm. Finally, a parallel version of the automated morphological endmember extraction algorithm is also presented and discussed. This algorithm integrates the spatial and spectral information as opposed to the other discussed algorithms, a feature that introduces additional considerations for its parallelization. The proposed algorithms are quantitatively compared and assessed in terms of both endmember extraction accuracy and parallel efficiency, using standard AVIRIS hyperspectral datasets. Performance data are measured on Thunderhead, a parallel supercomputer at NASA's Goddard Space Flight Center.
机译：光谱末端成员的自动提取是高光谱数据分析中的关键任务。在大多数情况下，端成员提取算法的计算复杂度非常高，尤其是对于非常高维的数据集而言。但是，可用技术的固有属性适合并行实现的设计。在这封信中，我们评估了几种并行算法，这些算法代表了解决端成员提取问题的三种代表性方法。已经选择了两个并行算法来表示基于凸几何概念的第一类算法。特别是，我们开发了N-FINDR和像素纯度指数算法的近似版本的并行实现，以及这两种技术的并行混合。第二类由基于约束错误最小化的算法给出，并由迭代错误分析算法的并行版本表示。最后，还提出并讨论了自动形态端成员提取算法的并行版本。与其他讨论的算法相反，此算法集成了空间和光谱信息，此功能为并行化引入了其他注意事项。使用标准AVIRIS高光谱数据集，对提出的算法进行了定量比较，并根据端成员提取精度和并行效率进行了评估。性能数据是在美国宇航局戈达德太空飞行中心的并行超级计算机Thunderhead上测量的。

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