Textural classification based on wavelet, curvelet and contourlet features

Multi-resolution analysis (MRA) has been successfully used in image processing with the recent emergence of applications to texture classification. Several studies have investigated the discriminating power of wavelet-based features in various applications such as image compression, image denoising, and classification of natural textures. Recently, the curvelet and contourlet transforms have emerged as new multi-resolution analysis tools to deal with non-linear singularities present in the image. This article explores and proposes a texture based classification of remotely sensed multispectral images using features derived from the wavelet, curvelet and contourlet transforms. These features characterize the textural properties of the images and are used to train the classifier to recognize each texture class. Using these MRA based feature descriptors class separability is defined in feature space. The results are compared with Grey Level Co-occurrence Matrix (GLCM) based statistical features.