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Big Data for Remote Sensing: Challenges and Opportunities

机译：遥感大数据：挑战与机遇

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摘要

Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.
机译：每天，来自许多不同国家的大量地球观测（EO）星载和机载传感器都提供了大量的遥感数据。这些数据用于不同的应用程序，例如自然灾害监测，全球气候变化，城市规划等。这些应用程序是数据驱动的，并且大多是跨学科的。基于此，可以说我们正处在大数据遥感时代。此外，这些数据正在成为许多应用程序中的经济资产和新的重要资源。在本文中，我们专门分析了大数据在遥感应用环境中带来的挑战和机遇。我们的重点是分析大数据在遥感应用中的确切含义以及在这种情况下大数据如何提供附加值。此外，本文还介绍了在管理，处理和有效利用大数据解决遥感问题方面最具挑战性的问题。为了说明上述方面，展示了两个讨论在遥感中使用大数据的案例研究。在第一个测试案例中，大数据被用于使用大型遥感数据档案自动检测海洋溢油。在第二个测试案例中，使用高性能计算（HPC）执行基于内容的信息检索，以从恐怖事件袭击纽约市世界贸易中心后收集的大型遥感图像数据库中提取信息。这两种情况均用于说明在遥感应用中使用大数据所带来的重大挑战和机遇。

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  • 来源
    《Proceedings of the IEEE》 |2016年第11期|2207-2219|共13页
  • 作者

    Mingmin Chi; Antonio Plaza; Jón Atli Benediktsson; Zhongyi Sun; Jinsheng Shen; Yangyong Zhu;

  • 作者单位

    School of Computer Science, Shanghai Key Laboratory of Data Science, Key Laboratory for Information Science of Electromagnetic Waves (MoE), State Key Laboratory of Satellite Ocean Environment Dynamics, Fudan University, Second Institute of Oceanography (SOA), Shanghai, Hangzhou, ChinaChina;

    Department of Technology of Computers and Communications, Escuela Politécnica de Cáceres, University of Extremadura, Cáceres, Spain;

    Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland;

    School of Computer Science, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China;

    School of Computer Science, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China;

    School of Computer Science, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Big data; Remote sensing; Sensors; Context modeling; Information retrieval; Computer applications; Terrorism; High performance computing;

    机译：大数据;遥感;传感器;上下文建模;信息检索;计算机应用;恐怖主义;高性能计算;

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