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Cloud implementation of the K-means algorithm for hyperspectral image analysis

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Abstract

Remotely sensed hyperspectral imaging offers the possibility to collect hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Hyperspectral images are characterized by their large volume and dimensionality, which makes their processing and storage difficult. As a result, several techniques have been developed in previous years to perform hyperspectral image analysis on high-performance computing architectures. However, the application of cloud computing techniques has not been as widespread. There are many potential advantages in exploiting cloud computing architectures for distributed hyperspectral image analysis. In this paper, we present a cloud implementation (developed using Apache Spark) of the popular K-means algorithm for unsupervised hyperspectral image clustering. The experimental results suggest that cloud architectures allow for the efficient distributed processing of large hyperspectral image data sets.

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Notes

  1. http://www.enmap.org/.

  2. http://hadoop.apache.org.

  3. http://spark.apache.org/.

  4. https://wiki.openstack.org/wiki/Main_Page.

  5. https://spark.apache.org/docs/latest/mllib-guide.html.

  6. Since the K-means may not find the optimal overall solution, it is recommended to run it several times to converge to a better final solution. So, if \(runs>1\), for each iteration the total number of sets with different centroids that will be executed equals the number of runs.

  7. After iterating, the algorithm takes only the best solution reached.

  8. Available online: https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html.

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Acknowledgments

The authors would like to take this opportunity to gratefully thank the Editors and Anonymous Reviewers for their outstanding comments and suggestions, which greatly helped us improve the technical quality and presentation of the manuscript. This work has been supported by the Spanish Ministry of Science and Education (FPU grants). This work has also been supported by Junta de Extremadura (GR15005 grant). We acknowledge the use of the computing facilities at Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF), and particularly the system administrators Abel Francisco Paz Gallardo and Alfonso Pardo Diaz.

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Authors and Affiliations

  1. Department of Technology of Computers and Communications, University of Extremadura, Escuela Politecnica, Avda. de la Universidad s/n, Cáceres, Spain

    Juan Mario Haut, Mercedes Paoletti, Javier Plaza & Antonio Plaza

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  1. Juan Mario Haut
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  2. Mercedes Paoletti
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  3. Javier Plaza
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  4. Antonio Plaza
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Correspondence to Javier Plaza.

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Haut, J.M., Paoletti, M., Plaza, J. et al. Cloud implementation of the K-means algorithm for hyperspectral image analysis. J Supercomput 73, 514–529 (2017). https://doi.org/10.1007/s11227-016-1896-3

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  • DOI: https://doi.org/10.1007/s11227-016-1896-3

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