Hierarchical pattern recognition of landform elements considering scale adaptation

Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies. Differential geometry method has been extensively applied in prior landform element research, while its efficacy in differentiating similar morphological characteristics remains inadequate to date. To reduce reliance on geomorphometric variables and increase awareness of landform patterns, geomorphons method was generated in previous study corresponding to specific landform reclassification map based on lookup table. Besides, to address the problem of feature similarity, hierarchical classification was proposed and effectively utilized for terrain recognition through the analytical strategy of fuzzy gradient features. Thus, combining the advantages of these two aspects, a hierarchical framework was proposed in this study for landform element pattern recognition considering the morphology and hierarchy factors. First, the local triplet patterns derived from geomorphons were enhanced by setting the flatness threshold, and subsequently adopted for the primary landform element recognition. Then, as geomorphic units with the same morphology possess different spatial analytical scales, the unidentified landform elements under the principle of scale adaptation were determined by calculating the spatial correlation and entropy information. To ensure the effectiveness of this proposed method, the sampling points were randomly selected from NASADEM data and then validated against a real 3D terrain model. Quantitative results of landform element pattern recognition demonstrate that our approach can reach above 77% average accuracy. Additionally, it delineates local details more effectively than geomorphons in visual assessment, resulting in a 7% accuracy improvement in overall scale.