Using GF-2 imagery and the conditional random field model for urban forest cover mapping

	Using GF-2 imagery and the conditional random field model for urban forest cover mapping
	Wang, Hao 1; Wang, Chengbo 1; Wu, Honggan 1
刊名	Remote Sensing Letters
	2016
卷号	7 期号:4 页码:378-387
通讯作者	Wang, Chengbo (wangcb@radi.ac.cn)
英文摘要	Gaofen-2 (GF-2), a Chinese new-generation satellite launched in August 2014, is providing high-resolution imagery for Earth observation. In this study, GF-2 imagery was employed for mapping forest cover in the core area of Tongzhou district, Beijing, China. The study analysed the performance of GF-2 data for identifying urban forest using a contextual classification model conditional random field (CRF) with Gabor texture features. The results show that the proposed method outperforms the traditional maximum likelihood classifier (MLC) by improving the producer's accuracy of conifer and hardwood forest from 86.61% to 92.41%, and 86.59% to 91.57%, respectively. Overall, 87.43% of the area classified as forest by GF-2 classification spatially corresponded to areas of the reference forest map. The mapping results suggest that GF-2 imagery in concert with an efficient classification algorithm can be recommended for urban forest monitoring. © 2016 Taylor & Francis.
收录类别	EI
语种	英语
WOS记录号	WOS:20161802323810
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39613]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing, China
推荐引用方式 GB/T 7714	Wang, Hao,Wang, Chengbo,Wu, Honggan. Using GF-2 imagery and the conditional random field model for urban forest cover mapping[J]. Remote Sensing Letters,2016,7(4):378-387.
APA	Wang, Hao,Wang, Chengbo,&Wu, Honggan.(2016).Using GF-2 imagery and the conditional random field model for urban forest cover mapping.Remote Sensing Letters,7(4),378-387.
MLA	Wang, Hao,et al."Using GF-2 imagery and the conditional random field model for urban forest cover mapping".Remote Sensing Letters 7.4(2016):378-387.