Abstract

This paper presents feature extraction and classification of multiclass images by using Haar wavelet transform and back propagation neural network. The wavelet features are extracted from original texture images and corresponding complementary images. The features are made up of different combinations of sub-band images, which offer better discriminating strategy for image classification and enhance the classification rate.

References

• Gong, P. and Howarth, P.J.,”Frequency-based contextual classification and gray-level vector reduction for land-use identification” Photogrammetric Engineering and Remote Sensing, 58, pp. 423–437. 1992.
• Kontoes C., Wilkinson G.G., Burrill A., Goffredo, S. and Megier J, “An experimental system for the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture” International Journal of Geographical Information Systems, 7, pp. 247–262, 1993.
• Foody G.M., ”Approaches for the production and evaluation of fuzzy land cover classification from remotely-sensed data” International Journal of Remote Sensing, 17,pp. 1317–1340 1996.
• San miguel-ayanz, J. and Biging, G.S,” An iterative classification approach for mapping natural resources from satellite imagery” International Journal of Remote Sensing, 17, pp. 957–982 , 1996.
• Aplin P., Atkinson P.M. and Curran P.J,” Per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors: problems and potential solutions” Advances in Remote Sensing and GIS Analysis, pp. 219–239,1999.
• Stuckens J., Coppin P.R. and Bauer, M.E.,”Integrating contextual information with per-pixel classification for improved land cover classification” Remote Sensing of Environment, 71, pp. 282–296, 2000.
• Mojsilovic A., Popovic M.V. and D. M. Rackov, “On the selection of an optimal wavelet basis for texture characterization”, IEEE Transactions on Image Processing, vol. 9, pp. 2043–2050, December 2000.
• M. Unser, “Texture classification and segmentation using wavelets frames”, IEEE Transactions on Image Processing, vol. 4, pp. 1549–1560, November 1995.
• Bishop H., Schneider, W. Pinz A.J., “Multispectral classification of Landsat –images using Neural Network”, IEEE Transactions on Geo Science and Remote Sensing. 30(3), 482-490, 1992.
• Heerman.P.D.and Khazenie, “Classification of multi spectral remote sensing data using a back propagation neural network”, IEEE trans. Geosci. Remote Sensing, 30(1),81-88,1992.
• Hepner.G.F., “Artificial Neural Network classification using minimal training set: comparision to conventional supervised classification” Photogrammetric Engineering and Remote Sensing, 56,469-473,1990.
• Mohanty.k.k. and Majumbar. T.J.,”An Artificial Neural Network (ANN) based software package for classification of remotely sensed data”, Computers and Geosciences, 81-87, 1996.
• Shamik Tiwari, Ajay Kumar Singh and V P Shukla, “Statistical Moments based Noise Classification using Feed Forward Back Propagation Neural Network”, International Journal of Computer Applications 18(2):36-40, March 2011.
• Stéphane G. Mallat, 1999, “A wavelet tour of signal processing”, Academic Press, 1999.
• Chin-Chen Chang, Jun-Chou Chuang and Yih-Shin Hu, “Similar Image Retrieval Based On Wavelet Transformation, International Journal Of Wavelets”, Multiresolution And Information Processing, Vol. 2, No. 2, 2004, pp.111–120, 2004.
• Charles K. Chui, “An Introduction to Wavelets”, Academic Press, 1992.
• Kumar S., ”Neural Networks: A Classroom Approach”, 1st Edn., Tata Mc-Graw Hill Publications, ISBN: 0-07-048292-6, pp: 169,2004.
• Montiel E., Aguado A. S., M. S. Nixon, “Texture classification via conditional histograms”, Pattern Recognition Letters, 26, pp. 1740-1751(2005).
• Brodatz P., ”Textures: A Photographic Album for Artist & Designers”, New York: Dover, New York, 1966.