Abstract
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.
Highlights
INTRODUCTIONObject-based image analysis (OBIA) has become a widely spread concept for many landforms classification studies based on DEM recent years (Drăguţ and Eisank, 2012; Whiteside and Ahmad, 2005; Manakos et al, 2000; Blaschke, 2010; Van Niekerk, 2010)
This study proposes an improved object-based classification for Chinese landform types
Based on 1000 resolution DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method
Summary
Object-based image analysis (OBIA) has become a widely spread concept for many landforms classification studies based on DEM recent years (Drăguţ and Eisank, 2012; Whiteside and Ahmad, 2005; Manakos et al, 2000; Blaschke, 2010; Van Niekerk, 2010). Lucian Drăguţ and Clemens Eisank (Drăguţ and Eisank, intersections precisely according to a grid interval of 28.125′′ (Longitude difference) × 18.750 ′′ (Latitude difference) based on the Chinese 1:50000 and 1:100000 topographic map (Song, 2006). This data has a high sampling accuracy and can reflect the terrain relief of China primely (Liu and Tang, 2012).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
