Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Real-Time Big Data Analytics for Remote Sensing Applications

Author : Bergi Veeresh Gowda 1 Niveditha J.N 2 K M Shiva Prasad 3

Date of Publication :17th April 2018

Abstract: Big data is high speed data with continuous stream. They are from various sources that include Internet, mobile devices, social media, geospatial devices, sensors, and other machine generated data. Big Data phenomenon and has created initiatives to exploit Big Data in many areas such as science and engineering, healthcare and national security. Presently a day's remote senses advanced world produce huge amount of volume of constant information called as "Large Data, Enormous data generated by Satellite sensors, Storage and Processing of Remote Sensing Data which is a challenging task due to its variety and volume. This project studies on real-time Big Data Analytical architecture for remote sensing satellite application. To control Remote Sensing Data proposed architecture which includes three main units, such as remote sensing big data acquisition unit (RSDU), Data processing unit(DPU) and Data analysis decision unit(DADU).First, RSDU is initial process which collects data from satellite and sends data to the base station. Second, DPU which play a virtual role in architecture for efficient processing of real time big data by providing filtration, load balancing, and parallel processing. Third, DADU which is responsible for compilation, storage of the results and it is placed in the upper layer unit of the proposed architecture. This architecture has the ability of dividing, load balancing, and parallel processing of only useful data. Thus, it results in efficiently analyzing real time remote sensing big data using earth observatory system. Furthermore, the real time big data architecture which as ability of storing incoming raw data to perform offline analysis on largely stored dumps, finally the analysis of remotely sensed earth observatory big data for land and sea areas provided by using Hadoop. In additionally various algorithms are proposed for each level of RSDU, DPU, DADU to detect land and sea or ice Area for remote sensing big data images.

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