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

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• 作者：Juan Mario Haut ; Mercedes Paoletti ; Javier Plaza…
• 关键词：Hyperspectral imaging ; Cloud computing ; K ; means clustering
• 刊名：The Journal of Supercomputing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：73
• 期：1
• 页码：514-529
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Programming Languages, Compilers, Interpreters; Processor Architectures; Computer Science, general;
• 出版者：Springer US
• ISSN：1573-0484
• 卷排序：73

文摘

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.