【论文摘要】The value of the high-resolution data lies in the high-precision information discovery. The fine-detailed landform element ex- traction 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 re- search 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 approach and their corresponding aspect-enhanced ap- proaches 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 as- pect-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 seg- mentation. The value of aspect is significantly great to be worthy of attentions in landform representation.
【论文信息】XIE Xiao, ZHOU Xiran, XUE Bing, XUE Yong, QIN Kai, LI Jingzhong, YANG Jun, 2021. Aspect in Topography to Enhance Fine-detailed Landform Element Extraction on High-resolution DEM. Chinese Geographical Science, 31(5): 915−930. https://doi.org/10.1007/s11769-021-1233-5
Fig. 1 Location of study areas in America and illustration of study area datasets