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Improvement of satellite image classification: Approach based on Hadoop/MapReduce

机译:卫星图像分类的改进:基于Hadoop / MapReduce的方法

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The revolution of technologies (social networks, smartphones, GPS and Remote Sensing Image) increase the volume of informations wich makes humanity in new need “Storage of huge volume of data”. the traditional strategy to store data become problem for humanity and then this need build new art to resolve this problems the “Spatial Big Data (SBD)” SBD store proncipally three types of data:vector data, raster data and network data. The complexity and nature of spatial databases make them ideal for applying parallel processing. This also emphasizes the need for developing new efficient geospatial analytic for analyzing spatial big data. So, we review the most used spatial data algorithms attracting human interests especially when the amount of satellite images continues to grow as more information becomes available. In this context, we propose a system based on Hadoop an open source system that implements the MapReduce programming model and that can improve the classification of large scale remote sensing image and benefit the power of spatial big data concept.
机译:技术的革命(社交网络,智能手机,GPS和遥感图像)增加了信息量,这使人类迫切需要“存储大量数据”。传统的数据存储策略成为人类的难题,因此需要建立新的技术来解决这一问题。“空间大数据(SBD)” SBD主要存储三种类型的数据:矢量数据,栅格数据和网络数据。空间数据库的复杂性和性质使其成为应用并行处理的理想选择。这也强调了开发新的有效地理空间分析以分析空间大数据的必要性。因此,我们回顾了吸引人们关注的最常用的空间数据算法,尤其是当随着更多信息的获得而卫星图像的数量持续增长时。在这种情况下,我们提出了一个基于Hadoop的开源系统,该系统实现了MapReduce编程模型,并且可以改善大规模遥感影像的分类,并受益于空间大数据概念的强大功能。

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