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Effective identification of terrain positions from gridded DEM data using multimodal classification integration

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posted on 11.01.2019, 16:55 authored by Ling Jiang, Dequan Ling, Mingwei Zhao, Chun Wang, Qiuhua LiangQiuhua Liang, Kai Liu
Terrain positions are widely used to describe the Earth’s topographic features and play an important role in the studies of landform evolution, soil erosion and hydrological modeling. This work develops a new multimodal classification system with enhanced classification performance by integrating different approaches for terrain position identification. The adopted classification approaches include local terrain attribute (LA)-based and regional terrain attribute (RA)-based, rule-based and supervised, and pixel-based and object-oriented methods. Firstly, a double-level definition scheme is presented for terrain positions. Then, utilizing a hierarchical framework, a multimodal approach is developed by integrating different classification techniques. Finally, an assessment method is established to evaluate the new classification system from different aspects. The experimental results, obtained at a Loess Plateau region in northern China on a 5 m digital elevation model (DEM), show reasonably positional relationship, and larger inter-class and smaller intra-class variances. This indicates that identified terrain positions are consistent with the actual topography from both overall and local perspectives, and have relatively good integrity and rationality. This study demonstrates that the current multimodal classification system, developed by taking advantage of various classification methods, can reflect the geographic meanings and topographic features of terrain positions from different levels.

History

School

  • Architecture, Building and Civil Engineering

Published in

ISPRS International Journal of Geo-Information

Volume

7

Issue

11

Citation

JIANG, L. ... et al, 2018. Effective identification of terrain positions from gridded DEM data using multimodal classification integration. ISPRS International Journal of Geo-Information, 7 (11), 443.

Publisher

MDPI © The authors

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

12/11/2018

Publication date

2018-11-14

Notes

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

eISSN

2220-9964

Language

en