Open AccessDistributed Framework for Processing High-Resolution Remote Sensing Images
Author(s) -
Thandu Nagaraju,
Ch. Suneetha
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.j9976.109119
Subject(s) - terabyte , computer science , block (permutation group theory) , process (computing) , data processing , remote sensing , satellite , image processing , real time computing , artificial intelligence , database , image (mathematics) , geology , engineering , geometry , mathematics , aerospace engineering , operating system
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.