Complex image processing using correlated color information

Abstract

The paper presents a method for patch classification and remote image segmentation based on correlated color information. During the training phase, a supervised learning algorithm is considered. In the testing phase, we used the classifier built a priori to predict which class an input image sample belongs to. The tests showed that the most relevant features are contrast, energy and homogeneity extracted from the co-occurrence matrix between H and S components. Compared to gray-level, the chromatic matrices improve the process of texture classification. For experimental results, the images were acquired by the aid of an unmanned aerial vehicle and represent various types of terrain. Two case studies have shown that the proposed method is more effective than considering separate color channels: flooded area and road segmentation. Also it is shown that the new algorithm provides a faster execution time than the similar one proposed.