A Set of Methods to Support Object-Based Distributed Analysis of Large Volumes of Earth Observation Data

作者：Ferreira Rodrigo S; Bentes Cristiana; Costa Gilson A O P^*; Oliveira Dario A B; Happ Patrick N; Feitosa Raul Q; Gamba Paolo

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(2): 681-690.

DOI：10.1109/JSTARS.2016.2636362

• Distributed processing
• image analysis
• remote sensing

The rapid increase in the number of aerial and orbital Earth observation systems is generating a huge amount of remote sensing data that need to be readily transformed into useful information for policy and decision makers. This exposes an urgent demand for image interpretation tools that can deal efficiently with very large volumes of data. In this work, we introduce a set of methods that support distributed processing of georeferenced raster and vector data in a computer cluster, which may be a virtual cluster provided by cloud computing infrastructure services. The set of methods comprise a particular technique for indexing distributed georeferenced datasets, as well as strategies for distributing efficiently the processing of spatial context-aware operations. They provide the means for the development of scalable applications, capable of processing large volumes of geospatial data. We evaluated the proposed methods in a remote sensing image interpretation application, built on the MapReduce framework, and executed in a cloud computing infrastructure. The experimental results corroborate the capacity of the methods to support efficient handling of very large earth observation datasets.

• 出版日期2017-2
• 单位IBM