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IJMLC 2012 Vol.2(6): 844-847 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.250

Performance Assessment of Neural Network and K-Nearest Neighbour Classification with Random Subwindows

M. Seetha, K. V. N. Sunitha, and G. Malini Devi

Abstract—Satellite image classification plays an important role in remote sensing, where information of an object or phenomenon is acquired from real time sensing devices, such as satellites and spacecrafts. In satellite image classification, the goal is to correctly classify vegetation, agriculture, water bodies, urban and open areas. The features like mean, Euclidean distance, RGB and slope values are extracted for each pixel in the input image. The image is classified using back propagation algorithm which reduces the misclassification that occurs in pixel based classification. This paper emphasizes on the classification of IRS 1-D LISS-III images using neural network, k- nearest neighbor and k- nearest neighbor with subwindows. An experimental comparison of neural network approach with back propagation algorithm was made with other considerations of the k-nearest neighbor and with subwindows. The results show that the k-nearest neighbor with subwindows has better overall accuracy and kappa coefficient when compared to neural networks.

Index Terms—Image classification, neural networks, remote sensing, spatial data, classification accuracy.

The authors are with the Dept. of CSE, GNITS, Hyderabad-8, India (e-mail: smaddala2000@yahoo.com; k.v.n.sunitha@gmail.com; malini_g12@yahoo.co.in).

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Cite:M. Seetha, K. V. N. Sunitha, and G. Malini Devi, "Performance Assessment of Neural Network and K-Nearest Neighbour Classification with Random Subwindows," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 844-847, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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