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Classification of hyperspectral imagery using MapReduce on a NVIDIA graphics processing unit (Conference Presentation)
Author(s): Andres Ramirez; Maryam Rahnemoonfar

Paper Abstract

A hyperspectral image provides multidimensional figure rich in data consisting of hundreds of spectral dimensions. Analyzing the spectral and spatial information of such image with linear and non-linear algorithms will result in high computational time. In order to overcome this problem, this research presents a system using a MapReduce-Graphics Processing Unit (GPU) model that can help analyzing a hyperspectral image through the usage of parallel hardware and a parallel programming model, which will be simpler to handle compared to other low-level parallel programming models. Additionally, Hadoop was used as an open-source version of the MapReduce parallel programming model. This research compared classification accuracy results and timing results between the Hadoop and GPU system and tested it against the following test cases: the CPU and GPU test case, a CPU test case and a test case where no dimensional reduction was applied.

Paper Details

Date Published: 9 June 2017
PDF: 1 pages
Proc. SPIE 10213, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017, 102130D (9 June 2017); doi: 10.1117/12.2268363
Show Author Affiliations
Andres Ramirez, TAMUCC (United States)
Maryam Rahnemoonfar, Texas A&M Univ Corpus Christi (United States)


Published in SPIE Proceedings Vol. 10213:
Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017
David P. Bannon, Editor(s)

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