![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
Classification of Remote Sensing Images using Wavelet Based Contourlet Transform and Accuracy Analysis of Classified Images
In remote sensing classification of spatial and spectral feature of multispectral images with high accuracy provide greater performance analysis. Wavelet transform is the most preferred transform for classification of both spectral and spatial features. The difficulty present here is the non-availability of directional features. The proposed wavelet based contourlet transform provide salient feature extraction using laplacian pyramid followed by directional filter banks. The extracted features were greatly reduced by using principle component analysis method. By that features the multispectral image has been classified into urban, wasteland, waterbody, hilly region by using fuzzy-c-means clustering algorithm. The wavelet transform is used for low frequency component classification and contourlet transform is used for high frequency component classification on the remote sensing images in the existing method. The aforesaid transforms provide less classification accuracy for remote sensing images. So it is proposed that the wavelet based contourlet transform is to be used for the analysis of both high frequency and low frequency component classification. Hence the proposed method shows that classification accuracy is higher than the existing method.
Keywords
Multispectral Image, Contourlet Transform, Wavelet Transfor, Fuzzy-C-Means Clustering Algorithm &PCA.
User
Font Size
Information
- Aditsharma,Khunteta (2016),” Satellite Image Contrast and Resolution Enhancement using Discrete Wavelet Transform and Singular Value Decomposition”, International Conference on Emerging Trends in Electrical, Electronics and Sustainable Energy Systems (ICETEESES–16)
- Ahmad, F., Ahmed, Z., & Najam, A. (2013), “Soft biometric gender classification using face for real time surveillance in cross dataset environment”. IEEE International Conference on Multi Topic, pp. 131–135.
- A. Alaguraja , K. Venkateswaran, N. Kasthuri1, R. (2015), “Performance Comparison of Wavelet and Contourlet Frame Based Features for Improving Classification Accuracy in Remote Sensing Images”, Journal Indian Social Remote Sensing.
- Alagu Raja, R. A., Anand, V., Maithani, S., SenthilKumar, A., & AbhaiKumar, V. (2009), “Wavelet frame based feature extraction technique for improving classification accuracy”, Journal of the Indian Society of Remote Sensing, 37(3), 423–443.
- Amelard, R., Wong A., & Clausi, D.A. (2013), “Unsupervised classification of agricultural land cover using polarimetric synthetic aperture radar via a sparse texture dictionary model”, IEEE International Conference on Geoscience and Remote Sensing symposium, pp. 4383–4386.
- Asli Ozdarici Ok, Ozlem Akar and Oguz Gungor (2012)” Evaluation of random forest method for agricultural crop classification”, European Journal of Remote Sensing - 2012, 45: 421-432 doi: 10.5721/EuJRS20124535
- Czajkowska, J., Bugdol, M., & Pietka, E. (2012), “Kernelized fuzzy C- means method and Gaussian mixture model in unsupervised cascade clustering”, Information Technologies in Biomedicine, 7339, 58 –66.
- Deshpande, D.S., Rajurkar, A.M., & Manthalkar, R.M. (2013), “Medical image analysis an attempt for mammogram classification using texture based association rule mining”, IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics. pp. 1–5.
- Do, M. N., & Vetterli, M. (2005), “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions Image on Processing, 14(12), 2091– 2106
- Dos.Santos,J.A.,Penatti,O.A.B.,DaTorres,R.S.,Go sselin,P,PhilippFoliguet,S.,&Falco,A.(2012), “Improving texture description in remote sensing image multi-scale classification tasks by using visual words”, IEEE International Conference on Pattern Recognition. pp. 3090–3093.
- Heinrich, A., Gen, D., Znamenskiy, D., Vink, J. P., & de Haan, G. (2014), “Robust and sensitive video motion detection for sleep analysis”, IEEE Journal on Biomedical and Health Informatics, 18(3), 790– 798.
- Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002a), “Cluster validity methods: Part-I”, ACM SIGMOD Record, 31(2), 40–45.
- Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002b), “Clustering validity checking methods: part II”, ACM SIGMOD Record, 31(3), 19–27.
- Lillesand, M.T., Ralph Kiefer, W., & Jonathan Chipman, W., (2004), “Remote Sensing and Image Interpretation”, 5th edition, Wiley International edition.
- Mioulet, L., Breckon, T.P., Mouton, A., Haichao Liang, & Morie, T. (2013), “Gabor features for real-time road environment classification”, IEEE International Conference on Industrial Technology. pp. 1117– 1121.
- Meher, S. K., Uma Shankar, B., & Ghosh, A. (2007), “Wavelet-feature- based classifiers for multispectral remote-sensing images”, IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1881– 1886
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 239
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0