Microlandform classification method for grid DEMs based on support vector machine

Abstract

Microlandform classification of grid digital elevation models (DEMs) is the foundation of digital landform refinement applications. To solve the shortcomings of the traditional regular grid DEM microlandform classification method, including low automation and incomplete classification results, a support vector machine (SVM) classifier was designed for grid DEM microlandform classification, and an automatic grid-based DEM microlandform classification method based on the SVM method was created. The experiment applies the SVM-based grid DEM microlandform classification method to identify different hill positions, namely, the summit, shoulder, back-slope, foot-slope, toe-slope, and alluvium. The results show that this method is most efficient in identifying the toe-slope, with an accuracy rate of 99.60%, and least efficient in identifying the foot-slope, with an accuracy rate of 98.18%. The kappa coefficient and model evaluation index F1-score verify that the method and model are reliable when applied to grid DEM microlandform classification problems.