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Article

A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

1
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
Lianyungang E-Port Information Development Co. Ltd., Lianyungang 222042, China
3
Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, 10003 Caceres, Spain
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3627; https://doi.org/10.3390/s18113627
Received: 4 September 2018 / Revised: 6 October 2018 / Accepted: 23 October 2018 / Published: 25 October 2018
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data. View Full-Text
Keywords: hyperspectral images; anomaly detection; distributed and parallel computing; apache spark; clouds hyperspectral images; anomaly detection; distributed and parallel computing; apache spark; clouds
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MDPI and ACS Style

Zhang, Y.; Wu, Z.; Sun, J.; Zhang, Y.; Zhu, Y.; Liu, J.; Zang, Q.; Plaza, A. A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images. Sensors 2018, 18, 3627. https://doi.org/10.3390/s18113627

AMA Style

Zhang Y, Wu Z, Sun J, Zhang Y, Zhu Y, Liu J, Zang Q, Plaza A. A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images. Sensors. 2018; 18(11):3627. https://doi.org/10.3390/s18113627

Chicago/Turabian Style

Zhang, Yi, Zebin Wu, Jin Sun, Yan Zhang, Yaoqin Zhu, Jun Liu, Qitao Zang, and Antonio Plaza. 2018. "A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images" Sensors 18, no. 11: 3627. https://doi.org/10.3390/s18113627

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