Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Automatic Object Recognition from Satellite Images using Artificial Neural Network

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 95 - Number 10
Year of Publication: 2014
Anil Kumar Goswami
Shalini Gakhar
Harneet Kaur

Anil Kumar Goswami, Shalini Gakhar and Harneet Kaur. Article: Automatic Object Recognition from Satellite Images using Artificial Neural Network. International Journal of Computer Applications 95(10):33-39, June 2014. Full text available. BibTeX

	author = {Anil Kumar Goswami and Shalini Gakhar and Harneet Kaur},
	title = {Article: Automatic Object Recognition from Satellite Images using Artificial Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {95},
	number = {10},
	pages = {33-39},
	month = {June},
	note = {Full text available}


Object recognition from satellite images is a very important application for various purposes. Objects can be recognized based on certain features and then applying some algorithm to extract those objects. Basically object recognition is a classification problem. For various remote sensing applications, waterbody acts as an important object which needs to be extracted. It is wise and better if possible, to extract waterbody object automatically from satellite data without any human intervention. This can be achieved using machine learning techniques. Artificial Neural Network (ANN) is such technique which makes machine intelligent by providing learning to it. This intelligent machine can extract objects automatically. This paper presents a methodology to extract waterbody object from satellite data in an automatic manner with the help of ANN. Training and testing dataset have been created by a domain expert which then have been used to train Multi Layer Perceptron (MLP) using Error Back Propagation (EBP) learning algorithm. Confusion matrix and Kappa coefficient have been used for accuracy assessment.


  • J. Vieira, F. M. Dias, A. Mota, Ding and W. Marchionini, "Neuro-Fuzzy Systems: A Survey ", A Study on Video Browsing Strategies, Technical Report, University of Maryland at College Park, 1997
  • S. Haykin, "Neural Networks: A Comprehensive Foundation", NY: Macmillan, Phi Learning Pvt. Ltd. , pp. 2, 1994.
  • J. M. Zurada, "Introduction to Artificial Neural Systems", Boston: PWS Publishing Company, preface pp. 15, 1992.
  • C. M. Bishop, "Neural Networks for Pattern Recognition", Oxford University Press, pp. 116-122, 1995.
  • R. D. Reed, and R. J. Marks II, "Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks", Cambridge, MA: The MIT Press, ISBN 0- 262- 181908, 1999.
  • D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation", ICS Report 8506,California University, San Diego, La Jolla, Institute for Cognitive Science, 1985.
  • D. E. Rumelhart, and J. L. McClelland, "Parallel Distributed Processing: Explorations in the Microstructure of Cognition", Cambridge, MA: The MIT Press, vol. 1, pp. 318-362, 1986.
  • V. Cherkassky and F. Mulier, "Learning from Data: Concepts, Theory and Methods", New York: Wiley, pp 156- 213, 1998.
  • L. Bruzzone and S. B. Serpico, "Classification of imbalanced remote-sensing data by neural networks", Pattern Recognition Letters, vol. 18 no. 11, pp. 1323-1328, 1997.
  • M. D. Emmerson and R. I. Damper, "Determining and Improving the Fault Tolerance of Multilayer Perceptrons in a Pattern-Recognition Application", IEEE Transactions on Neural Networks, vol. 4 no. 5, pp. 788-793, 1993.
  • J. Yi and R. Prybutok, "A neural network model forecasting for prediction of maximum ozone concentration in an industrialized urban area", Environmental Pollution, vol. 92 no. 3, pp. 349-357, 1996.
  • Marzban and G. J. Stumpf, "A neural network for tornado prediction based on Doppler radar derived attributes", Journal of Applied Meteorology, vol. 35, pp. 617-626, 1996.
  • H. D. Navone and H. A. Ceccatto, "Predicting Indian monsoon rainfall: a neural network approach", Climate Dynamics, vol. 10, pp. 305-312, 1994.
  • R. M. Welch, S. K. Sengupta, A. K. Goroch, P. Rabindra, N. Rangaraj and M. S. Navar , "Polar cloud classification using AVHRR imagery - an inter comparison of methods", Journal of Applied Meteorology, vol. 31, pp. 405-420, 1992.
  • J. E. Peak and P. M. Tag, "Towards automated interpretation of satellite imagery for navy shipboard applications", Bulletin of the American Meteorological Society, vol. 73 no. 7, pp. 955-1008, 1992.