Extraction of loess shoulder-line based on the parallel GVF snake model in the loess hilly area of China

Loess shoulder-lines are the most critical terrain feature in representing and modeling the landforms of the Loess Plateau of China. Existing algorithms usually fail in obtaining a continuous shoulder-line for complicated surface, DEM quality and algorithm limitation. This paper proposes a new method, by which gradient vector flow (GVF) snake model is employed to generate an integrated contour which could connect the discontinuous fragments of shoulder-line. Moreover, a new criterion for the selection of initial seeds is created for the snake model, which takes the value of median smoothing of the local neighborhood regions. By doing this, we can extract the adjacent boundary of loess positive-negative terrains from the shoulder-line zones, which build a basis to found the real shoulder-lines by the gradient vector flow. However, the computational burden of this method remains heavy for large DEM dataset. In this study, a parallel computing scheme of the cluster for automatic shoulder-line extraction is proposed and implemented with a parallel GVF snake model. After analyzing the principle of the method, the paper develops an effective parallel algorithm integrating both single program multiple data (SPMD) and master/slave (M/S) programming modes. Based on domain decomposition of DEM data, each partition is decomposed regularly and calculated simultaneously. The experimental results on different DEM datasets indicate that parallel programming can achieve the main objective of distinctly reducing execution time without losing accuracy compared with the sequential model. The hybrid algorithm in this study achieves a mean shoulder-line offset of 15.8m, a quite satisfied result in both accuracy and efficiency compared with published extraction methods.