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Scientific Programming
Volume 2016 (2016), Article ID 3252148, 9 pages
Research Article

Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark

1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China

Received 24 February 2016; Revised 6 May 2016; Accepted 22 May 2016

Academic Editor: Laurence T. Yang

Copyright © 2016 Zebin Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS), and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.