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Journal of Applied Remote Sensing • new

Parallel optimization of pixel purity index algorithm for massive hyperspectral images in cloud computing environment
Author(s): Yufeng Chen; Zebin Wu; Le Sun; Zhihui Wei; Yonglong Li
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Paper 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.

Paper Details

Date Published: 20 June 2016
PDF: 14 pages
J. Appl. Remote Sens. 10(2) 025024 doi: 10.1117/1.JRS.10.025024
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Yufeng Chen, Nanjing Univ. of Science and Technology (China)
Nanjing Branch of China Telecom Corp. (China)
Zebin Wu, Nanjing Univ. of Science and Technology (China)
Le Sun, Nanjing Univ. of Information Science & Technology (China)
Zhihui Wei, Nanjing Univ. of Science and Technology (China)
Yonglong Li, Nanjing Univ. of Science and Technology (China)

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