Processing of Large Satellite Images using Hadoop Distributed Technology and Mapreduce : A Case of Edge Detection

Abstract

Now a day's amount of data continues to grow as more information becomes available. The Exponential growth of data and the increasing user’s demand for real time satellite data has forced remote sensing service providers to deliver the required services. The processing of large amount of images is necessary when there are satellite images involved.This paper presents a distributed technology, mapreduce programming paradigm,which is based on Hadoop platform to process large-scale satellite images.The main aim of this hadoop concept is to take the advantage of high reliability and high scalability in the field of remote sensing as to achieve the purpose of fast processing of large satellite images.The Hadoop streaming technology is used in the model and the main operations are written on java as the mapper and reducer.The model has been implemented using virtual machines where the large number of images are delivered to the multicluster nodes for concurrent processing.This paper presents a MapReduce based processing of large satellite images using edge detection methods .Sobel, Laplacian, and Canny edge detection methods are implemented in this model.
DOI: 10.17762/ijritcc2321-8169.1505203