Distributed Hyperspectral Image Analysis based on Cloud Computing Architectures

In this thesis, we introduce a novel distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral remote sensing data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package capabilities, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for performing endmember extraction processes, which can be likewise executed on cloud computing environments, allowing users to elastically access and exploit processing power and storage space within cloud computing architectures, for adequately processing large volumes of hyperspectral data. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, assessing both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating new endmember extraction algorithms within the proposed architecture, thus enabling researchers to implement their own distributed endmember extraction approaches specifically designed for processing large volumes of hyperspectral data