基于Hadoop框架的高分辨率卫星图像处理

2015 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2015-11-01 DOI:10.1109/CCEM.2015.16

Roshan Rajak, Deepu Raveendran, Maruthi Chandrasekhar Bh, S. Medasani

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引用次数: 16

摘要

使用Hadoop MapReduce框架的并行和分布式处理，可以高效地处理需要较高计算能力和大规模输入的复杂图像处理算法。Hadoop MapReduce是一个可扩展的模型，能够处理pb(1015个数量级)的数据，并具有改进的容错性和数据并行性。本文提出了一个MapReduce框架，利用Hadoop对遥感卫星数据进行并行处理，并将处理结果存储在HBase中。加速和性能表明，通过使用Hadoop，我们可以将工作负载分布到不同的集群上，以利用普通硬件上的综合处理能力。

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High Resolution Satellite Image Processing Using Hadoop Framework

Complex image processing algorithms that require higher computational power with large scale inputs can be processed efficiently using the parallel and distributed processing of Hadoop MapReduce Framework. Hadoop MapReduce is a scalable model which is capable of processing petabytes (1015 order) of data with improved fault tolerance and data parallelism. In this paper we present a MapReduce framework for performing parallel remote sensing satellite data processing using Hadoop and storing the output in HBase. The speedup and performance show that by utilizing Hadoop, we can distribute our workload across different clusters to take advantage of combined processing power on commodity hardware.

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2015 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

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