Cloud implementation of the K means algorithm for hyperspectral image analysis
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.
The Journal of Supercomputing