Chinese Geographical Science ›› 2021, Vol. 31 ›› Issue (5): 915-930.doi: 10.1007/s11769-021-1233-5

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Aspect in Topography to Enhance Fine-detailed Landform Element Extraction on High-resolution DEM

XIE Xiao1,2,3,4, ZHOU Xiran1,3, XUE Bing2, XUE Yong1,3, QIN Kai1,3, LI Jingzhong2, YANG Jun5   

  1. 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
    2. Key Lab for Environmental Computation and Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China;
    3. Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China;
    4. School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China;
    5. JangHo Architecture, Northeastern University, Shenyang 110169, China
  • Received:2020-12-18 Published:2021-09-02
  • Contact: ZHOU Xiran E-mail:xiexiao@iae.ac.cn
  • Supported by:
    Under the auspices of Priority Academic Program Development of Jiangsu Higher Education Institutions (No. 140119001), Science & Technology Department of Liaoning Province (No. 20180550831)

Abstract: The value of the high-resolution data lies in the high-precision information discovery. The fine-detailed landform element extraction is thus the basis of high-fidelity application of the high-resolution digital elevation models (DEMs). However, the results of landform element extraction generated by classical methods might be ungrounded on high-resolution DEMs. This paper presents our research on using the aspect to reinforce the applicability and robustness of the classical approaches in landform element extraction. First, according to the research of pattern recognition, we assume that aspect-enhanced landform representation is robust to rotation, scaling and affine variance. To testify the role of aspect, we respectively integrated the aspect into three classical approaches: mean curvature-based fuzzy classification, elevation-based feature descriptor, and object-based segmentation. In the experiment, based on four types of high-resolution DEMs (1 m, 2 m, 4 m and 8 m), we compare each classical approaches and their corresponding aspect-enhanced approaches based on extracting the rims of two craters having different landforms, and the ridgelines and valleylines of a region covered by few vegetables and man-made buildings. In comparison to the results generated by curvature-based fuzzy classification, the aspect enhanced curvature-based fuzzy classification can effectively filter a number of noises outperforms the curvature-based one. Otherwise, the aspect-enhanced feature descriptor can detect more landform elements than the elevation-based feature descriptor. Moreover, the aspect-based segmentation can detect the main structure of landform, while the boundaries segmented by classical approaches are messing and meaningless. The systematic experiments on meter-level resolution DEMs proved that the aspect in topography could effectively to improve the classical method-system, including fuzzy-based classification, feature descriptors-based detection and object-based segmentation. The value of aspect is significantly great to be worthy of attentions in landform representation.

Key words: high-resolution DEM (digital elevation model), landform representation, landform element extraction, crater detection, aspect granularity, aspect-enhanced landform representation, America