Advertisement

Earth Science Informatics

, Volume 11, Issue 1, pp 31–45 | Cite as

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

  • Wadii BoulilaEmail author
  • Imed Riadh Farah
  • Amir Hussain
Research Article

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.

Keywords

Decision support system Remote sensing data Image interpretation ETL process Data warehouse Predictive analytic Descriptive analytics Prescriptive analytics 

References

  1. Ai F, Comfort LK, Dong Y, Znati T (2016) A dynamic decision support system based on geographical information and mobile social networks: a model for tsunami risk mitigation in Padang Indonesia. Saf Sci 90:62–74CrossRefGoogle Scholar
  2. Aimaiti Y, Kasimu A, Jing G (2016) Urban landscape extraction and analysis based on optical and microwave ALOS satellite data. Earth Sci Inf 9:425–435CrossRefGoogle Scholar
  3. Ait El Mekki O, Laftouhi N (2016) Combination of a geographical information system and remote sensing data to map groundwater recharge potential in arid to semi-arid areas: the Haouz plain. Morocco, Earth Sci Inf 9:465–479CrossRefGoogle Scholar
  4. Alaa AM, Moon KH, Hsu W, Member MVDS (2016) Confident Care: A Clinical Decision Support System for Personalized Breast Cancer Screening. IEEE Trans Multimedia 18(10):1942–1955CrossRefGoogle Scholar
  5. Alcón JF, Ciuhu C, Kate WT, Heinrich A, Uzunbajakava N, Krekels G, Siem D, Haan GD (2009) Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE J Sel Top Sign Proces 3(1):14–25CrossRefGoogle Scholar
  6. Bhardwaj A, Sam L, Bhardwaj A, Martín-Torres FJ (2016) LiDAR remote sensing of the cryosphere: present applications and future prospects. Remote Sens Environ 177:125–143CrossRefGoogle Scholar
  7. Bodart C, Eva H, Beuchle R, Raši R, Simonetti D, Stibig HJ, Brink A, Lindquist E, Achard F (2011) Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics. ISPRS J Photogramm Remote Sens 66:555–563CrossRefGoogle Scholar
  8. Boulila W, Ettabaa KS, Farah IR, Solaiman B, Ben Ghézala H (2009) Towards a multi-approach system for uncertain spatio-temporal knowledge discovery in satellite imagery, international journal on graphics. Vis Image Proc 9(06):19–25Google Scholar
  9. Boulila W, Farah IR, Saheb Ettabaa K, Solaiman B, Ben Ghézala H (2010) Spatio-temporal modeling for knowledge discovery in satellite image databases, CORIA COnférence en Recherche d'Information et Applications, Sousse, 35–49Google Scholar
  10. Boulila W, Farah IR, Solaiman B, Ben Ghézala H (2011a) Interesting spatiotemporal rules discovery: application to remotely sensed image databases. VINE J Inf Knowl Manag Syst 41(2):167–191Google Scholar
  11. Boulila W, Farah IR, Saheb Ettabaa K, Solaiman B, Ben Ghézala H (2011b) A data mining based approach to predict spatiotemporal changes in satellite images. Int J Appl Earth Obs Geoinf 13(3):386–395Google Scholar
  12. Boulila W, Bouatay A, Farah IR(2014) A probabilistic collocation method for the imperfection propagation: application to land cover change prediction. J Multimedia Process Technol 5(1):12–32Google Scholar
  13. Cavallaro G, Riedel M, Richerzhagen M, Benediktsson JA, Plaza A (2015) On understanding big data impacts in remotely sensed image classification using support vector machine methods. IEEE J Sel Top Appl Earth Obs Remote Sen 8(10):4634–4646CrossRefGoogle Scholar
  14. Dempere-Marco L, Hu XP, MacDonald SLS, Ellis SM, Hansell DM, Yang GZ (2002) The Use of Visual Search for Knowledge Gathering in Image Decision Support. IEEE Trans Med Imaging 21(7):741–754CrossRefGoogle Scholar
  15. Farah IR, Boulila W, Saheb Ettabaa K, Solaiman B, Ben Ahmed M (2008) Interpretation of multi-sensor remote sensing images: multi-approach fusion of uncertain information. TGRS IEEE Trans Geosci Remote Sens 46(12):4142–4152CrossRefGoogle Scholar
  16. Fassnacht FE, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser LT, Straub C, Ghosh A (2016) Review of studies on tree species classification from remotely sensed data. Remote Sens Environ 186:64–87CrossRefGoogle Scholar
  17. Fegraus EH, Zaslavsky I, Whitenack T, Dempewolf J, Ahumada JA, Lin K, Andelman SJ (2012) Interdisciplinary decision support dashboard: a new framework for a Tanzanian agricultural and ecosystem service monitoring system pilot. IEEE J Sel Top Appl Earth Obs Remote Sens 5(6):1700–1708CrossRefGoogle Scholar
  18. Ferchichi A, Boulila W, Farah IR (2017a) Propagating aleatory and epistemic uncertainty in land cover change prediction process. Eco Inform 37:24–37CrossRefGoogle Scholar
  19. Ferchichi A, Boulila W, Farah IR (2017b) Towards an uncertainty reduction framework for land-cover change prediction using possibility theory. Vietnam J Comput Sci 4(3):195–209CrossRefGoogle Scholar
  20. Giachetta R (2015) A framework for processing large scale geospatial and remote sensing data in MapReduce environment. Comput Graph 49:37–46CrossRefGoogle Scholar
  21. Heb Y, Ai B, Yao Y, Zhong F (2015) Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images. Int J Appl Earth Obs Geoinf 38:164–174CrossRefGoogle Scholar
  22. Hoque MA, Phinn S, Roelfsema C, Childs I (2017) Tropical cyclone disaster management using remote sensing and spatial analysis: a review. Int J Disaster Risk Reduct 22:345–354CrossRefGoogle Scholar
  23. Hwangbo JW, Yu K (2010) Decision support system for the selection of classification methods for remote sensing imagery. KSCE J Civ Eng 14(4):589–600CrossRefGoogle Scholar
  24. Khanal S, Fulton F, Shearer S (2017) An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture 139:22–32Google Scholar
  25. Kimball R, Ross M (2013) The data warehouse toolkit: the definitive guide to dimensional modeling, John Wiley & Sons, IndianapolisGoogle Scholar
  26. Kouzes RT, Anderson GA, Elbert ST, Gorton I, Gracio DK (2009) The changing paradigm of data-intensive computing. Computer 42(1):26–34Google Scholar
  27. Leonard A, Masson M, Mitchell T, Moss JM, Ufford M (2012) Data cleansing with data quality services, SQL server 2012 integration services design patterns. Apress publishing, pp 101–122Google Scholar
  28. Leonard A, Masson M, Mitchell T, Moss JM, Ufford M (2014) Data correction with data quality services, SQL server 2012 integration services design patterns. Apress publishing, pp 101–123Google Scholar
  29. Licciardi GA, Del Frate F (2011) Pixel Unmixing in hyperspectral data by means of neural networks. IEEE Trans Geosci Remote Sens 49(11):4163–4172CrossRefGoogle Scholar
  30. Liu Y, Wu L (2016) Geological disaster recognition on optical remote sensing images using deep learning. Procedia Comput Sci 91:566–575CrossRefGoogle Scholar
  31. Ma Y, Wang L, Liu P, Ranjan R (2015a) Towards building a data-intensive index for big data computing – a case study of remote sensing data processing. Inf Sci 319:171–188CrossRefGoogle Scholar
  32. Ma Y, Wu H, Wang L, Huang B, Ranjan R, Zomaya A, Jie W (2015b) Remote sensing big data computing: challenges and opportunities. Futur Gener Comput Syst 51:47–60CrossRefGoogle Scholar
  33. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations, proceedings of 5th Berkeley symposium on mathematical statistics and probability, University of California Press, pp. 281–297Google Scholar
  34. Malik ZK, Hussain A, Wu J (2016) An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data. Neurocomputing 173:127–136CrossRefGoogle Scholar
  35. Microsoft: Data quality services, SQL Server 2012 books online, http://msdn.microsoft.com/en-us/library/ff877925.aspx
  36. Minelli M, Chambers M, Dhiraj A (2013) Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses. Wiley Publishing, HobokenCrossRefGoogle Scholar
  37. Moller-Jensen L (1997) Classification of urban land cover based on expert systems, object models and texture. Comput Environ Urban Syst 21:291–302CrossRefGoogle Scholar
  38. Platt T, Sathyendranath S (2008) Ecological indicators for the pelagic zone of the ocean from remote sensing. Remote Sens Environ 112:3426–3436CrossRefGoogle Scholar
  39. Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-Valls G, Chanussot J, Fauvel M, Gamba P, Gualtieri A, Marconcini M, Tilton JC, Trianni G (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:S110–S122CrossRefGoogle Scholar
  40. Ramírez-Cuesta JM, Cruz-Blanco M, Santos C, Lorite IJ (2017) Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools. Int J Appl Earth Obs Geoinf 55:32–42CrossRefGoogle Scholar
  41. Rathore MMU, Paul A, Ahmad A, Chen BW, Huang B, Ji W (2015) Real-time big data analytical architecture for remote sensing application. IEEE J Sel Topics Appl Earth Obs Remote Sen 8(10):4610–4621CrossRefGoogle Scholar
  42. Réjichi S, Chaabane F, Tupin F (2015) Expert knowledge-based method for satellite image time series analysis and interpretation. IEEE J Sel Top Appl Earth Obs Remote Sens 8(5):2138–2150CrossRefGoogle Scholar
  43. Rhee J, Im J (2017) Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agric For Meteorol 237–238:105–122CrossRefGoogle Scholar
  44. Sharifi A (1999) Remote sensing and decision support systems, Spatial Statistics for Remote Sensing. Remote Sensing and Digital Image Processing 1:243–260Google Scholar
  45. Sun Z, Zou H, Strang K (2015) Big data analytics as a Service for Business Intelligence, Open and Big Data Management and Innovation. Lecture Notes in Computer Science 9373:200–211CrossRefGoogle Scholar
  46. Talia D (2013) Clouds for scalable big data analytics. Computer 46(5):98–101CrossRefGoogle Scholar
  47. Verbesselt J (2015) Big data: Techniques and technologies in geoinformatics. In: Karimi HA (ed) International Journal of Applied Earth Observation and Geoinformation 35(part B). CRC Press, Taylor & Francis, London, pp 368–369Google Scholar
  48. Zhang J, Li T, Lu X, Cheng Z (2016) Semantic classification of high-resolution remote-sensing images based on mid-level features. IEEE J Sel Earth Obs Remote Sen 9(6):2343–2353CrossRefGoogle Scholar
  49. Zhao S, Wang Q, Li Y, Liu S, Wang Z, Zhu L, Wang Z (2017) An overview of satellite remote sensing technology used in China’s environmental protection. Earth Sci Inf 10:137–148CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Wadii Boulila
    • 1
    • 2
    Email author
  • Imed Riadh Farah
    • 1
    • 2
  • Amir Hussain
    • 3
  1. 1.RIADI Laboratory, National School of Computer SciencesUniversity of ManoubaManoubaTunisia
  2. 2.ITI Department, Telecom-BretagneUniversity of Rennes 1BrestFrance
  3. 3.Division of Computing Science & Maths, School of Natural SciencesUniversity of StirlingStirlingUK

Personalised recommendations