In order to solve the problem that the clustering centers cannot be determined automatically by Density Peaks Clustering (DPC) algorithm, and the clustering center points and the non-clustering center points are not obvious enough in the decision graph, Comparative density Peaks Clustering algorithm with Automatic determination of clustering center (ACPC) was designed. Firstly, the distance parameter was replaced by the distance comparison quantity, so that the potential clustering centers were more obvious in the decision graph. Then, the 2D interval estimation method was used to perform the automatic selection of clustering centers, so as to realize the automation of clustering process. Experimental results show that the ACPC algorithm has better clustering effect on four synthetic datasets; and the comparison of the Accuracy indicator on real datasets shows that on the dataset Iris, the clustering accuracy of ACPC can reach 94%, which is 27.3% higher than that of the traditional DPC algorithm, and the problem of selecting clustering centers interactively is solved by ACPC.

Most of the exiting hillshading algorithms are implemented based on single-core single-thread programming model, which makes them have lower computational efficiency. To solve this problem, an improved algorithm for parallelizing the existing hillshading algorithms based on multi-core programming model was proposed. Firstly, the original Digital Elevation Model (DEM) data were divided into several data blocks by grid segmentation. Secondly, these data blocks were shaded in parallel using the class Parallel under the .Net environment to generate shaded image of each block. Finally, the shaded images were spliced into a complete hillshading image. The experimental results show that the calculation efficiency of the improved parallelized algorithm is obviously higher than that of the existing shading algorithms based on single-core single-thread programming, and there is an approximate linear growth relation between the number of the involved cores and the shading efficiency. Additionally, it is also found that the three dimensional and realistic effect of the hillshading image is extremely relevant to the parameter setting of the light source.