Contourlet based Hyperspectral Image Classification

Abstract

Classification of hyperspectral images in remote sensing has received attention during past decades. In this work, the feature extraction within images is based on Contourlet Transform (CT) and the classification is based on Support Vector Machine (SVM).  In the existing system discrete wavelet transform is used to get detailed information with spectral and spatial characteristics of a pixel. But, it does not provide information about features in its directional components.  The proposed system, to extract these features, Contourlet transform based laplacian pyramid followed by directional filter banks are used for feature extraction. Initially, the input hyper spectral image is decomposed into four sub bands by the application of stationary wavelet transform. Then the GLCM features are extracted from sub bands. The remaining sub bands are subjected to directional filter bank. The better classification is arrived by extracting and selecting the best features from the Contourlet Coefficients of the image and the outputs are used as an input to the Support Vector Machine classifier for classification with high accuracy.