A multiple-point spatially weighted k-NN classifier for remote sensing

	A multiple-point spatially weighted k-NN classifier for remote sensing
	Tang, Yunwei 1; Jing, Linhai 1; Atkinson, Peter M.1; Li, Hui 1
刊名	International Journal of Remote Sensing
	2016
卷号	37 期号:18 页码:4441-4459
关键词	SEA-SURFACE TEMPERATURE PACIFIC-OCEAN VARIABILITY CLIMATE
通讯作者	Tang, Yunwei (tangyw@radi.ac.cn)
英文摘要	A novel classification method based on multiple-point statistics (MPS) is proposed in this article. The method is a modified version of the spatially weighted k-nearest neighbour (k-NN) classifier, which accounts for spatial correlation through weights applied to neighbouring pixels. The MPS characterizes the spatial correlation between multiple points of land-cover classes by learning local patterns in a training image. This rich spatial information is then converted to multiple-point probabilities and incorporated into the k-NN classifier. Experiments were conducted in two study areas, in which the proposed method for classification was tested on a WorldView-2 sub-scene of the Sichuan mountainous area and an IKONOS image of the Beijing urban area. The multiple-point weighted k-NN method (MPk-NN) was compared to several alternatives; including the traditional k-NN and two previously published spatially weighted k-NN schemes; the inverse distance weighted k-NN, and the geostatistically weighted k-NN. The classifiers using the Bayesian and Support Vector Machine (SVM) methods, and these classifiers weighted with spatial context using the Markov random field (MRF) model, were also introduced to provide a benchmark comparison with the MPk-NN method. The proposed approach increased classification accuracy significantly relative to the alternatives, and it is, thus, recommended for the identification of land-cover types with complex and diverse spatial distributions. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
学科主题	Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20163102677031
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39319]
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. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. Faculty of Science and Technology, Engineering Building, Lancaster University, Lancaster, United Kingdom 3. School of Geography, Archaeology and Palaeoecology, Queen’s University Belfast, Northern Ireland, United Kingdom 4. Geography and Environment, University of Southampton, Southampton, United Kingdom
推荐引用方式 GB/T 7714	Tang, Yunwei,Jing, Linhai,Atkinson, Peter M.,et al. A multiple-point spatially weighted k-NN classifier for remote sensing[J]. International Journal of Remote Sensing,2016,37(18):4441-4459.
APA	Tang, Yunwei,Jing, Linhai,Atkinson, Peter M.,&Li, Hui.(2016).A multiple-point spatially weighted k-NN classifier for remote sensing.International Journal of Remote Sensing,37(18),4441-4459.
MLA	Tang, Yunwei,et al."A multiple-point spatially weighted k-NN classifier for remote sensing".International Journal of Remote Sensing 37.18(2016):4441-4459.