Classification of Panchromatic Images Using Ripplet Transform and LBP Methods

Abstract

Ripplet transform is one of effective methods in texture feature extraction. Image classification is done in two steps: Image feature extraction and automatic classification of these features. In the feature extraction step, rippletI, rippletII, curvelet and ridgelet transforms were used. These transforms yield appropriate results in identifying borders and edges of the figures. Local binary pattern (LBP) method is a simple got accurate method for identifying index class distribution. Hence, using staking method (combine ripplet transform and LBP methods) results in higher number of features vectors and improves the classification accuracy as much as 5%. Support vector machine (SVM) classify is used in classification step. Experimental results has been performed two databases (south Tehran and brotza).