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A novel decision support system for the interpretation of remote sensing big data

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Abstract

Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the increase of aerial and satellite sensors. With this continuous increase, the necessity of having automated tools for the interpretation and analysis of RS big data is clearly obvious. The manual interpretation becomes a time consuming and expensive task. In this paper, a novel tool for interpreting and analyzing RS big data is described. The proposed system allows knowledge gathering for decision support in RS fields. It helps users easily make decisions in many fields related to RS by providing descriptive, predictive and prescriptive analytics. The paper outlines the design and development of a framework based on three steps: RS data acquisition, modeling, and analysis & interpretation. The performance of the proposed system has been demonstrated through three models: clustering, decision tree and association rules. Results show that the proposed tool can provide efficient decision support (descriptive and predictive) which can be adapted to several RS users’ requests. Additionally, assessing these results show good performances of the developed tool.

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Authors and Affiliations

  1. RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia

    Wadii Boulila & Imed Riadh Farah

  2. ITI Department, Telecom-Bretagne, University of Rennes 1, Brest, France

    Wadii Boulila & Imed Riadh Farah

  3. Division of Computing Science & Maths, School of Natural Sciences, University of Stirling, Stirling, UK

    Amir Hussain

Authors
  1. Wadii Boulila
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  2. Imed Riadh Farah
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  3. Amir Hussain
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Correspondence to Wadii Boulila.

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Communicated by: H. A. Babaie

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Boulila, W., Farah, I.R. & Hussain, A. A novel decision support system for the interpretation of remote sensing big data. Earth Sci Inform 11, 31–45 (2018). https://doi.org/10.1007/s12145-017-0313-7

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