Special Section on Progress in Snow Remote Sensing

Support vector machine-based decision tree for snow cover extraction in mountain areas using high spatial resolution remote sensing image

[+] Author Affiliations
Liujun Zhu

Nanjing University, Department of Geographic Information Science, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, and Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, 163 Xianlin Road, Nanjing 210023, China

Pengfeng Xiao

Nanjing University, Department of Geographic Information Science, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, and Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, 163 Xianlin Road, Nanjing 210023, China

Xuezhi Feng

Nanjing University, Department of Geographic Information Science, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, and Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, 163 Xianlin Road, Nanjing 210023, China

Xueliang Zhang

Nanjing University, Department of Geographic Information Science, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, and Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, 163 Xianlin Road, Nanjing 210023, China

Zuo Wang

Nanjing University, Department of Geographic Information Science, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, and Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, 163 Xianlin Road, Nanjing 210023, China

Luyuan Jiang

Nanjing University, Department of Geographic Information Science, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, and Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, 163 Xianlin Road, Nanjing 210023, China

J. Appl. Remote Sens. 8(1), 084698 (Apr 02, 2014). doi:10.1117/1.JRS.8.084698
History: Received November 28, 2013; Revised February 10, 2014; Accepted February 12, 2014
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Abstract.  Snow cover extraction in mountain areas is a complex task, especially from high spatial resolution remote sensing (HSRRS) data. The influence of mountain shadows in HSRRS is severe and normalized difference snow index-based snow cover extraction methods are inaccessible. A decision tree building method for snow cover extraction (DTSE) integrated with an efficiency feature selection algorithm is proposed. The severe influence of terrain shadows is eliminated by extracting snow in sunlight and snow in shadow separately in different nodes. In the feature selection algorithm, deviation of fuzzy grade matrix is proposed as a class-specific criterion which improves the efficiency and robustness of the selected feature set, thus making the snow cover extraction accurate. Two experiments are carried out based on ZY-3 image of two regions (regions A and B) located in Tianshan Mountains, China. The experiment on region A achieves an adequate accuracy demonstrating the robustness of the DTSE building method. The experiment on region B shows that a general DTSE model achieves an unsatisfied accuracy for snow in shadow and DTSE rebuilding evidently improves the performance, thus providing an accurate and fast way to extract snow cover in mountain areas.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Liujun Zhu ; Pengfeng Xiao ; Xuezhi Feng ; Xueliang Zhang ; Zuo Wang, et al.
"Support vector machine-based decision tree for snow cover extraction in mountain areas using high spatial resolution remote sensing image", J. Appl. Remote Sens. 8(1), 084698 (Apr 02, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.084698


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