ABSTRACT
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