Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 10, Issue 2, Pages 681-690Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2016.2636362
Keywords
Distributed processing; image analysis; remote sensing
Categories
Funding
- CNPq
- CAPES
- FAPERJ
- FP7 program, in the scope of the TOLOMEO Project
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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.
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