A Hadoop-based Distributed Framework for Efficient Managing and Processing Big Remote Sensing Images
نویسندگان
چکیده
Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the dataand computingintensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.
منابع مشابه
Overview of Hadoop in Remote Sensing Image Processing with Various Algorithms and Techniques in Cloud
In today’s era of big data, with the introduction of high resolution systems, remote sensing image processing is one of the fastest growing fields resulting in a rapid increase in volume of data being generated day by day. To handle such massive volumes of data, a high processing speed has become an indispensable requirement. This is possible with the help of big data platforms such as Hadoop. ...
متن کاملImage Data Classification using Hadoop Based on Semi Supervise Algorithm
In this paper, an technique is presented for storing and dispensation bulky satellite images by using the Hadoop MapReduce framework and HDFS(Hadoop distributed file system)by incorporate Remote Sensing image processing tools into MapReduce The huge volume of visual data in current years and their require for efficient and efficient processing arouse the exploit of distributed image processing ...
متن کاملEfficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm
The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Clou...
متن کاملCloud Computing Technology Algorithms Capabilities in Managing and Processing Big Data in Business Organizations: MapReduce, Hadoop, Parallel Programming
The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hos...
متن کاملAn Efficient Approach to Optimize the Performance of Massive Small Files in Hadoop MapReduce Framework
The most popular open source distributed computing framework called Hadoop was designed by Doug Cutting and his team, which involves thousands of nodes to process and analyze huge amounts of data called Big Data. The major core components of Hadoop are HDFS (Hadoop Distributed File System) and MapReduce. This framework is the most popular and powerful for store, manage and process Big Data appl...
متن کامل