A Data Mining Approach to Recognize Objects in Satellite Images to Predict Natural Resources

  • Muhammad Shahbaz
  • Aziz Guergachi
  • Aneela Noreen
  • Muhammad Shaheen
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)


This paper presents an approach for the classification of satellite images by recognizing various objects in them. Satellite images are rich in geographical information that can be used in a number of useful ways. The proposed system classifies satellites images by extracting different objects from the images. Our object recognition mechanism extracts attributes from satellite images under two domains namely: color pixels’ organization and pixel intensity. The extracted attributes aid in the identification of objects lying inside the satellite images. Once we are able to identify objects, we proceeded further to classify satellite images with the help of decision trees. The system has been tested for a number satellite images acquired from around the globe. The objects in the images have been further subdivided into different sub categories to improve the classification and prediction process. This is a novel approach which is not using any image processing techniques but is utilizing the extracted features to identify objects and then using these objects to classify the satellite images.


Classification of images Data mining Decision tree Machine learning Object recognition Satellite images 


  1. 1.
    Aghbari ZA (2009) Effective image mining by representing color histograms as time series. JACIII 13(2):109–114Google Scholar
  2. 2.
    Mustafa AAY, Shapiro LG, Ganter MA (1996) 3D object recognition from color intensity images. In: 13th International Conference on Pattern Recognition, Vienna, Austria, pp 25–30Google Scholar
  3. 3.
    Ozcan E, Mohan CK (1997) Partial shape matching using genetic algorithms. Pattern Recogn Lett Elsevier Sci 18:987–992Google Scholar
  4. 4.
    Farzin M, Abbasi S, Kittler J (1996) Robust and efficient shape indexing through curvature scale space. British Machine Vision Conference, Edinburgh, pp 53–62Google Scholar
  5. 5.
    Roh KS, Kweon IS (1998) 2-D object recognition using invariant contour descriptor and projective refinement. Pattern Recognition, Elsevier V31(4):441–455Google Scholar
  6. 6.
    Tsaneva M, Petkov D (2007) Recognition of objects on the Earth’s surface through texture analysis of satellite images. In: Proceeding of the third scientific conference with international participation Space, Ecology, Nanotechnology, Safety Varna, Bulgaria, pp 27–29Google Scholar
  7. 7.
    Bordes J-B, Roux M (2006) Detection of roundabouts in satellite image. ISPRS, Ankara (Turkey)Google Scholar
  8. 8.
    Talibi-Alaoui M, Sbihi A (2012) Application of a mathematical morphological process and neural network for unsupervised texture image classification with fractal features. IAENG Int J Comput Sci 39(3):286–294Google Scholar
  9. 9.
    Luo J, Hao W, McIntyre D, Joshi D, Yu J (2008) Recognizing picture-taking environment from satellite images: a feasibility study. Pattern Recognition, ICPRGoogle Scholar
  10. 10.
    Shahbaz M et al. (2012) Classification by object recognition in satellite images by using data mining. In: ’Lecture notes in engineering and computer science: proceedings of the world congress on engineering, WCE 2012, 4–6 July, London, UK, pp 406–416Google Scholar
  11. 11.
    Lorrentz P (2010) Classification of incomplete data by observation. Engineering Letters, ISSN: 1816-0948 (online) 1816-093x (print), 18(4):1–10Google Scholar
  12. 12.
    Seinstra FJ, Geusebroek J-M (2006) ECCV Workshop on computation Intensive Mthods for Computer Vision, 9th European Conference on Computer Vision, Graz, Austria, 7–13 May, 2006Google Scholar
  13. 13.
    Smith JR, Chang S-F (1996) Tools and techniques for color image retrieval. In: Symposium on electronic imaging: science and technology—storage and retrieval for image and video databases IV, vol. 2670, IS &T/SPIE, San Jose, USAGoogle Scholar
  14. 14.
    Gonzalez RC, Woods RE (2001) Digital image processing. 2nd edn. Prentice Hall, pp 120–139. ISBN:0201180758Google Scholar
  15. 15.
    Petrou M, Sevilla PG (2006) Image processing dealing with texture. Wiley, pp 282–529. ISBN: 047002628Google Scholar
  16. 16.
    Image Processing Toolbox, For use with MATLAB, The Maths Work Inc, 2001Google Scholar
  17. 17.
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656Google Scholar
  18. 18.
    Kenney JF, Keeping ES (1962) Mathematics of statistics. Pt. 1, 3rd edn. Princeton, Van NostrandGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Muhammad Shahbaz
    • 1
  • Aziz Guergachi
    • 2
  • Aneela Noreen
    • 3
  • Muhammad Shaheen
    • 4
  1. 1.Department of Computer Science and EngineeringUniversity of Engineering and Technology LahorePakistan
  2. 2.Information Technology ManagementTed Rogers School, Ryerson UniversityTorontoCanada
  3. 3.Department of Computer ScienceUniversity of Engineering and TechnologyLahorePakistan
  4. 4.Department of Computer ScienceFAST NUPeshawarPakistan

Personalised recommendations