An application of tree species classification using high-resolution remote sensing image based on the rough set theory

doi:10.1007/s11042-016-4210-8

	An application of tree species classification using high-resolution remote sensing image based on the rough set theory
	Zeng, Yi 1; Wang, Shuang 1; Zhao, Tianzhong 1; Wang, Jing 2
刊名	MULTIMEDIA TOOLS AND APPLICATIONS
	2017-11-01
卷号	76 期号:21 页码:22999-23015
关键词	Rough set theory Feature extraction Object-oriented classification Tree species recognition Forest monitoring
ISSN号	1380-7501
DOI	10.1007/s11042-016-4210-8
通讯作者	Zeng, Yi(zengyi@bjfu.edu.cn)
英文摘要	Feature extraction is an essential task in the classification of high-resolution remote sensing images, with the primary technique being the object-oriented classification method. Current research describes object-oriented classification methods by using remote sensing data, wherein how to reduce the redundant feature information to achieve good classification results is the most challenging problem. The high-resolution remote sensing image is characteristic of a large amount of data and high feature dimensions, which also exist particularly in the forestry remote sensing. Feature information redundancy can reduce the extraction accuracy and make the classification results worse. To address this problem, in this paper we propose a framework that uses the rough set theory and the membership function to establish the classification rule set. In our approach, we first select an optimal segmentation scale to segment the remote sensing image with multi-scale and apply the rough set theory to reduce the feature dimensions of objects. We then use the selected features to establish classification rule set and classify image objects. This paper also presents a detailed study of the proposed framework for species classification with ALOS images, wherein 13 most effective feature parameters are selected from 34 feature parameters of objects, such as band ratio, brightness value, and average gray value. Our experimental results demonstrate that the proposed framework, applied to classify tree species, achieves a classification accuracy of 80.4509%, which is an improvement over both the classification accuracy of 77.2408% achieved with the traditional supervised classification and that of 75.5068% achieved with the nearest neighbor classification. The research proves that the proposed framework can effectively take advantage of tree species information in remote sensing images, and provides an auxiliary means for forest resources investigation and monitoring.
资助项目	Beijing Natural Science Foundation[6164038] ; China Fundamental Research Funds for the Central Universities[TD2014-2]
WOS关键词	DIMENSIONALITY ; REDUCTION ; RULES
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000412748200057
资助机构	Beijing Natural Science Foundation ; China Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/62397]
专题	中国科学院地理科学与资源研究所
通讯作者	Zeng, Yi
作者单位	1.Beijing Forestry Univ, Coll Informat Sci, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resource & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zeng, Yi,Wang, Shuang,Zhao, Tianzhong,et al. An application of tree species classification using high-resolution remote sensing image based on the rough set theory[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2017,76(21):22999-23015.
APA	Zeng, Yi,Wang, Shuang,Zhao, Tianzhong,&Wang, Jing.(2017).An application of tree species classification using high-resolution remote sensing image based on the rough set theory.MULTIMEDIA TOOLS AND APPLICATIONS,76(21),22999-23015.
MLA	Zeng, Yi,et al."An application of tree species classification using high-resolution remote sensing image based on the rough set theory".MULTIMEDIA TOOLS AND APPLICATIONS 76.21(2017):22999-23015.