## 1 Introduction  
Rock masses behavior is governed by properties of rock surface including fractures, joints, faults and other geological structures. For a rock engineering research, it is necessary to make a detailed investigation for geological conditions. With the distribution, combination condition and geometrical information of rock mass, both engineering geological evaluation and rock mass classification would be proceeded well. Therefore, it is worthy for transportation engineering, hydropower engineering and mining engineering to extract rock surface accurately, efficiently and comprehensively, which is necessary for engineering design, exploration, construction and evaluation. However, for those dangerous and inaccessible regions, such as alpine and gorge, the traditional contact measurements could not be proceeded efficiently and safely with a limited time. Terrestrial Laser Scanning (TLS) and close-range photogrammetry, which are the mainly two non-contact active measurements, could collect point cloud or digital images data and achieve rock surface extraction more comprehensively and conveniently in a virtual digital environment [1; 2; 3; 4]. TLS could obtain an accurate 3D model of an object in high efficiency, and get the spatial coordinates of an object with high accuracy and speed, which could be used for several purposes [5]. With the advantages of high spatial resolution, high temporal resolution and uniform accuracy, the TLS data is more appropriate for many applications, especially for inaccessible and dangerous regions [3; 6]. However, as the TLS data is characterized as unstructured 3D point clouds, it is poor at expressing the object features. As another non-contact active measurement technique, photogrammetry is used to process stereoscopic images delivering RGB and spectral channel information, which could be considered largely complementary to TLS [7; 8]. So, the integration of the multi-source RS data has complementary advantages, which is valuable for geological investigation. Registration is one crucial problem that the research of rock surface extraction based on the integration of the multi-source RS data involves, as the correlation of the TLS point cloud and digital images could be obtained by registration. About the registration of the two data for geological objects, the authors have provided a detailed method in another article [9]. For the integration of multi-source RS data, it is also necessary to comprehensively analyze and evaluate multi-feature of the data and make a correct and efficient segmentation, which is the key of accurate, efficient and full extraction for rock surface. So, this paper mainly discusses the segmentation problem which the TLS point cloud and digital images integration is involved in. The first step to be finished is the construction of the direct mapping relationship between the two data sources. Texture mapping is a simple way for 3D visualization of the textured point cloud. However, the true mapping relationship for the point cloud and digital images could not be obtained. Therefore, exploring a new method for direct mapping relationship construction is the precondition of rock surface extraction. Furthermore, in order to get an efficient and accurate segmentation result, the difference as well as the correlation between the characteristics of geological objects in the Multi-source RS data should be taken full use of. At present, there is only so much research on point cloud or image segmentation, which would be valuable for the segmentation of multi-source RS data integration. The point cloud segmentation methods include edge-based segmentation [10; 11; 12], model fitting-based segmentation [13; 14; 15], region-based segmentation [16; 17; 18; 19; 20] and feature clustering-based segmentation [21; 22]. For the point cloud, the only spatial information is insufficient for segmentation.