Volume XLI-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 213-217, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-213-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 213-217, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-213-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jun 2016

21 Jun 2016

AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION

Hu Ding1,3, Fei Tao2, Wufan Zhao1,3, Jiaming Na1,3, and Guo’an Tang1,3 Hu Ding et al.
  • 1Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, No.1 Wenyuan Road, Nanjing, 210023, China
  • 2School of Geography Science, Nantong University, Nantong, 226019, China
  • 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, No.1 Wenyuan Road, Nanjing, 210023, China

Keywords: Landform Classification, DEM, Object-based, Random Forest, Gray-level Co-occurrence Matrix

Abstract. Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.