20 June 2016 Parallel optimization of pixel purity index algorithm for massive hyperspectral images in cloud computing environment
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Abstract
With the gradual increase in the spatial and spectral resolution of hyperspectral images, the size of image data becomes larger and larger, and the complexity of processing algorithms is growing, which poses a big challenge to efficient massive hyperspectral image processing. Cloud computing technologies distribute computing tasks to a large number of computing resources for handling large data sets without the limitation of memory and computing resource of a single machine. This paper proposes a parallel pixel purity index (PPI) algorithm for unmixing massive hyperspectral images based on a MapReduce programming model for the first time in the literature. According to the characteristics of hyperspectral images, we describe the design principle of the algorithm, illustrate the main cloud unmixing processes of PPI, and analyze the time complexity of serial and parallel algorithms. Experimental results demonstrate that the parallel implementation of the PPI algorithm on the cloud can effectively process big hyperspectral data and accelerate the algorithm.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yufeng Chen, Zebin Wu, Le Sun, Zhihui Wei, Yonglong Li, "Parallel optimization of pixel purity index algorithm for massive hyperspectral images in cloud computing environment," Journal of Applied Remote Sensing 10(2), 025024 (20 June 2016). https://doi.org/10.1117/1.JRS.10.025024 . Submission:
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