## 4 Experiment and Discussion
It is observed from the two local rectangle regions A and B that the minute fissure of rock stratum is processed more accurately with the MFF-SLIC algorithm, while under-segmentation could not be avoided well for the 2D SLIC algorithm, such as the fissures marked with cyan, the color and texture of which are similar to the surrounding area. Therefore, it is difficult to distinguish without the local geological attitude information. It is obvious that the MFF-SLIC algorithm considering local geological features has a better performance on quality segmentation. Moreover, superpixels segmented by the use of MFF-SLIC algorithm are all with geological attributes, which would make the next clustering process achieve higher efficiency and accuracy. Figure 18 shows the comparison of segmentation results with 1000 superpixels. It can be seen that MFF-SLIC still performs better boundary consistency. According to the above experimental results, it can be indicated that compared with 2D SLIC, the MFF-SLIC algorithm has precise and reliable advantages. It is worth mentioning that the difference between the two algorithms will not be obvious when the number of superpixels is too large. Figure 19 shows the comparison of segmentation results for the two algorithms when the number of superpixels is set to 2500. The size of the superpixel is so small that it is difficult to evaluate the performance of  
Figure 16: The 2D visualization of attitudes for a digital image: (**a**) The 2D visualization of dips in the image; (**b**) The 2D visualization of dip angles in the image. the two algorithms. Therefore, the number of superpixels may affect both efficiency and precision of the algorithm, which should be reasonably set according to image size and superpixel size. Too few superpixels would cause under-segmentation, which may decrease the rock surface extraction precision. Conversely, too many superpixels would cause over-segmentation, which may reduce segmentation efficiency. Besides, the size of superpixels is so small that the attitude difference between superpixels is not obvious, so it is difficult to distinguish valid rock surface. Figure 19: The comparison of segmentation results with 2500 superpixels. Figure 17: The comparison of segmentation results with 500 superpixels. Figure 18: The comparison of segmentation results with 1000 superpixels. Histograms for attitude distributions of some part superpixels are shown as Figure 20, the left column shows dip angle histogram distributions, and the right column shows dip histogram distributions. The horizontal axis represents the value of the dip angle or the dip, and the vertical axis represents pixel numbers corresponding to different dip angles or dips in a superpixel. All histograms indicate that attitudes of pixels in a superpixel are approximately consistent, i.e., most pixels in a superpixel have similar attitude characteristics, which demonstrates that the result of superpixel segmentation is reliable and accurate. Figure 13:0 The histogram for attitude distribution of individual superpixel. The test image size is 784 by 845 pixels, which is approximately determined by overlapping area, and the size and the number of superpixels are experimentally set to 442 (_pixels_) and 1500 (_superpixels_). The corresponding RAG and NNG for superpixel segmentation result are shown as Figure 13:1 The blue nodes represent superpixels, each red line segment connects two adjacent superpixels, and the weight of the red line segment is reflected by dissimilarity of the adjacent superpixels. With the RAG and NNG, the hierarchical and optimized clustering of superpixels could be achieved according to Section 3.3. Figure 22 shows the superpixels clustering result for the test image. After 20 iterations, the number of superpixels reduced to 118. The final segmentation results could be obtained through a further congruency analysis to all overlapping areas between the two data sources.