Distributed Framework for Processing High-Resolution Remote Sensing Images
T. Naga Raju1, Chittineni Suneetha2

1T. Nagaraju, Research Scholar Department of Computer Science & Engineering, Acharya Nagarjuna University, Guntur, (AP.), India.
2Dr. Ch. Suneetha, Associate Professor, Department of Computer Applications, RVR&JC College of Engineering, Chowdavaram, (AP.), India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 4287-4292 | Volume-9 Issue-1, October 2019 | Retrieval Number: J99760881019/2019©BEIESP | DOI: 10.35940/ijeat.J9976.109119
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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: Now-a-days, sensing of remote satellite data processing is a very challenging task. The current development of satellite technology has led to explosive growth in quantity as well as the quality of the High-Resolution Remote Sensing (HRRS) images. These images can sometimes be in Gigabytes and Terabytes, which is heavy to load into the memory and also takes more time for processing. To address the challenges of processing HRRS images, a distributed map Reduce framework is proposed in this paper. This paper reflects Map-reduce as a distributed model using the Hadoop framework for processing large amounts of images. To process large amounts of images, block-based and size-based methods are introduced for effective processing. From the experiments, the proposed framework has proven to be effective in performance and speed.
Keywords: Hadoop framework. High Resolution, Remote Sensing, Map-reduce.