The paper presents a method for patch classification and remoteimage segmentation based on correlated color information. During the trainingphase, a supervised learning algorithm is considered. In the testing phase, weused the classifier built a priori to predict which class an input image samplebelongs to. The tests showed that the most relevant features are contrast, energyand homogeneity extracted from the co-occurrence matrix between H and Scomponents. Compared to gray-level, the chromatic matrices improve the processof texture classification. For experimental results, the images were acquiredby 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 thanconsidering 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.