Rasterhadoop: An Application Perspective of Raster Data Processing on Hadoop
R. Phani Bhushan1, DVLN Somayajulu2, S Venkatraman3, RBV Subramanyam4
1R. Phani Bhushan, ADRIN, Dept. Of Space, Hyderabad, India.
2DVLN Somayajulu, Dept. of CSE, IIITDM, Kurnool, India.
3S. Venkatraman, ADRIN, Dept. Of Space, Hyderabad, India.
4RBV Subramanayam, Dept. Of CSE, NIT, Warangal, India.

Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11147-11150 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4304118419/2019©BEIESP | DOI: 10.35940/ijrte.D4304.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Hadoop is currently the most popular platform for parallel processing. With its two major components namely the Distributed File System (HDFS) and a parallel processing paradigm (MapReduce) in addition to its ease of installation and usage, Hadoop has become the chosen platform for efficiency whether in the commercial arena or the scientific arena such as Satellite Data Processing. The number of remote sensing satellites have also grown leaps and bounds and the data sent back by them for processing has all the three characteristics namely volume, velocity and variety that make it Big Spatial Data. In this paper, we present the extensions provided to Hadoop that enable Image Processing using legacy code and further elaborate on the various methods provided.
Keywords: Hadoop, Raster Data, Image Processing, Big Spatial Data.
Scope of the Article: Signal and Image Processing.