Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 213-217, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/213/2016/
doi:10.5194/isprs-archives-XLI-B7-213-2016
 
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 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.
Conference paper (PDF, 1530 KB)


Citation: Ding, H., Tao, F., Zhao, W., Na, J., and Tang, G.: AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 213-217, doi:10.5194/isprs-archives-XLI-B7-213-2016, 2016.

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