## 1 Introduction
Segmentation based on the depth image generated from point cloud would result in an expensive computational cost and information loss, which may affect the precision of segmentation result. For the image segmentation, there are mainly three groups: threshold-based segmentation [23; 24; 25], edge-based segmentation and region-based segmentation [26; 27]. Instead of the above segmentation methods in pixels, some scholars put forward the idea of superpixel segmentation, which is a preprocessing stage to group pixels into \"superpixels\" [28]. The superpixel is composed of a set of pixels with some similar features, including texture, brightness and color. The image feature description with superpixels could decrease the edge extraction difficulty of complex objects, and improve the efficiency of image segmentation. Generally, superpixel segmentation algorithms could be classified into: (1) graph-based, including Normalized cut, Superpixel Lattice and Entropy Rate; (2) gradient-ascent-based, including Watersheds, Mean-shift, Quick-shift, Turbopixels and SLIC (Simple Linear Iterative Clustering). All the above algorithms show different performance in efficiency, precision, boundary adherence and expansibility [29]. In comparison of segmentation algorithms in pixels, superpixel segmentation has a better consideration of the association between local regional features, merging and clustering based on which could improve the efficiency and precision of segmentation. However, as most of existing algorithms are proposed for image data, which only applies the 2D image features, the precision could not be guaranteed for those objects with the similar image features but different spatial or other features. Based on the above analysis, this paper develops a new hybrid superpixel segmentation algorithm, MFF-SLIC, for accurate, efficient and full extraction of rock surface. This algorithm firstly obtained the mapping relationship between the TLS point cloud and digital images according to the registration result; and then explored a new distance measure model for initial superpixel segmentation. The new distance measure model integrated multi-features of both two types' data, including color and pixel location of digital images as well as geological properties and spatial position of TLS point cloud. With the above distance measure model, the geological object could be divided into a number of superpixels which have certain semantic features; next, superpixels would be clustered hierarchically and optimally (i.e., the initial segmentation result would be further processed) to provide credible data for fine extraction of rock surface. At last, with the columnar basalt data (acquired in Guabushan Geopark in Nanjing, China), the segmentation method proposed in this paper was verified. The results show that the MFF-SLIC hybrid superpixel segmentation algorithm could achieve rock surface extraction accurately based on the integration of the Multi-source RS data, even without any manual interactions, which provides a valuable data basis for further geological statistics and analysis. The rest of this paper is organized as follows: Section 2 reviews the registration overview about multi-source RS data and the existing 2D SLIC algorithm for image segmentation. Section 3 describes the detailed methodology of the MFF-SLIC hybrid superpixel segmentation algorithm for rock surface extraction. In Section 4, an experiment with the columnar basalt data is conducted in order to illustrate the feasibility and applicability in geology of the algorithm. Section 5 draws the conclusion.