Performance Comparison of Wavelet and Contourlet Frame Based Features for Improving Classification Accuracy in Remote Se
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RESEARCH ARTICLE
Performance Comparison of Wavelet and Contourlet Frame Based Features for Improving Classification Accuracy in Remote Sensing Images K. Venkateswaran 1 & N. Kasthuri 1 & R. A. Alaguraja 2
Received: 19 June 2014 / Accepted: 27 April 2015 # Indian Society of Remote Sensing 2015
Abstract Conventional classification algorithms makes the use of only multispectral information in remote sensing image classification. Wavelet provides spatial and spectral characteristics of a pixel along with its neighbours and hence this can be utilized for an improved classification. The major disadvantage of wavelet transform is the non availability of spatial frequency features in its directional components. The contourlet transform based laplacian pyramid followed by directional filter banks is an efficient way of extracting features in the directional components. In this paper different contourlet frame based feature extraction techniques for remote sensing images are proposed. Principal component analysis (PCA) method is used to reduce the number of features. Gaussian Kernel fuzzy C-means classifiers uses these features to improve the classification accuracy. Accuracy assessment based on field visit data and cluster validity measures are used to measure the accuracy of the classified data. The experimental result shows that the overall accuracy is improved to 1.73 % (for LISS-II), 1.81 % (for LISS-III) and 1.95 % (for LISS-IV) and the kappa coefficient is improved to 0.933 (for LISS-II), 0.0103 (for LISS-III) and 0.0214 (for LISS-IV) and also the cluster validity measures gives better results when compared to existing method
* K. Venkateswaran [email protected] N. Kasthuri [email protected] R. A. Alaguraja [email protected] 1
Kongu Engineering College Perundurai, Erode, India
2
Thiagarajar College of Engineering, Madurai, India
Keywords Wavelet based contourlet transform (WBCT) . Feature extraction . Feature reduction . Feature classification . Accuracy assessment
Introduction Feature extraction is the process of generating features to be used in the selection and classification tasks. It has immense range of applications like remote sensing (Dos Santos et al. 2012), agricultural surveys (Amelard et al. 2013), environmental monitoring (Mioulet et al. 2013), motion detection (Heinrich et al. 2014), surveillance (Ahmad et al. 2013), medical diagnosis (Deshpande et al. 2013) etc. Three methods of feature extraction techniques viz., statistical, structural and spectral was widely followed. Statistical approaches yield characterizations of as smooth, coarse and grainy. Structural techniques deal with the arrangement of image primitives such as the description of features based on regularly spaced parallel lines. Spectral techniques were based on properties of the fourier spectrum primarily to detect global periodicity in an image by identifying high-energy, narrow peaks in the spectrum. Wavelet transform is one of the spectral based feature extraction technique and multi-resolution analytical tool for spat
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