Recognition and classification of land features on the images have been considered as the base of many applications including the development of a digital model of elevation, identification of changes, updating of maps and many other cases in geomatics. In recent years, researchers have tried to improve the accuracy of this process. By recognizing land features and classification of the image we mean the set of processes and operations which lead to identifying land features and attributing a sticker to each of the pixels entering the classification operation. Based on this, recognition and identification can be achieved by relying on the differences between objects in terms of characteristics recorded by different sensors. The more varied information is available, the more precise and reliable the results will be. Today, with the advancement of technology, various types of information are available by various sensors. But none of these sources provide all the textural, geometric, and spectral properties of an object. That's why it is inevitable to combine the information from different sensors to complete the descriptive space that leads to more accurate extraction of land features. In this study, the integration of digital aerial image information and Lidar data has been evaluated and its role in increasing the accuracy of classification has been tested using a data set from an area in Germany. The results show that the classification accuracy is increased by using digital aerial image and Lidar data simultaneously.