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Deep learning of DEM image texture for landform classification in the Shandong area, China

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Abstract

Landforms are an important element of natural geographical environment, and textures are the research basis for the spatial differentiation, evolution features, and analysis rules of the landform. Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification. Digital elevation model (DEM) image texture, which gives full expression to texture difference, is key data source to reflect the surface features and landform classification. Following the texture analysis, landform features analysis is assistant to different landforms classification, even in landform boundary. With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping, hierarchical landform classification has become the focus and difficulty in research. Recently, the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research, whose multichannel feature fusion structure satisfies the network structure of different landform classification. In this paper, DEM image texture was taken as the data source, and gray level co-occurrence matrix was applied to extract texture measures. Owing to the similarity of similar landform and the difference of different landform in a certain scale, a comprehensive texture factor reflecting landform features was proposed, and the spatial distribution pattern of landform features was systematically analyzed. On this basis, the coupling relationship between texture and landform type was explored. Thus, the deep learning method of Convolutional Neural Network is used to train the texture features, and the second-class landform classification is carried out through softmax. The classification results in small relief and mid-relief low mountains, overall accuracy are 84.35% and 69.95% respectively, while kappa coefficient are 0.72 and 0.40 respectively, were compared to that of traditional unsupervised landform classification results, and the superiority of Convolutional Neural Network classification was verified, it approximately improved 6% in overall accuracy and 0.4 in kappa coefficient.

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Acknowledgements

This work was supported by the auspices of the National Natural Science Foundation of China (Grant Nos. 41930102, and 41971339) and SDUST Research Fund (No. 2019TDJH103).

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Authors and Affiliations

  1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China

    Yuexue Xu, Hongchun Zhu, Changyu Hu & Yu Cheng

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Haiying Liu

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  1. Yuexue Xu
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  2. Hongchun Zhu
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Correspondence to Hongchun Zhu.

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Xu, Y., Zhu, H., Hu, C. et al. Deep learning of DEM image texture for landform classification in the Shandong area, China. Front. Earth Sci. 16, 352–367 (2022). https://doi.org/10.1007/s11707-021-0884-y

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