A multiple-point spatially weighted k-NN method for object-based classification

	A multiple-point spatially weighted k-NN method for object-based classification
	Tang, Yunwei 1; Jing, Linhai 1; Li, Hui 1; Atkinson, Peter M.1
刊名	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
	2016
卷号	52 页码:263-274
关键词	SPARSE SAHELIAN VEGETATION LAND-SURFACE EVAPOTRANSPIRATION RATES CANOPY RESISTANCE KB(-1) PARAMETER SOIL FORMULATION SEBS SYSTEMS LENGTH
通讯作者	Tang, YW ; Jing, LH (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China.
英文摘要	Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification. (C) 2016 Elsevier B.V. All rights reserved.
学科主题	Remote Sensing
类目[WOS]	Remote Sensing
收录类别	SCI
语种	英语
WOS记录号	WOS:000383003500024
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39169]
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.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China 2.Univ Lancaster, Fac Sci & Technol, Engn Bldg, Lancaster LA1 4YR, England 3.Queens Univ Belfast, Sch Geog Archaeol & Palaeoecol, Belfast BT7 1NN, Antrim, North Ireland 4.Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England
推荐引用方式 GB/T 7714	Tang, Yunwei,Jing, Linhai,Li, Hui,et al. A multiple-point spatially weighted k-NN method for object-based classification[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2016,52:263-274.
APA	Tang, Yunwei,Jing, Linhai,Li, Hui,&Atkinson, Peter M..(2016).A multiple-point spatially weighted k-NN method for object-based classification.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,52,263-274.
MLA	Tang, Yunwei,et al."A multiple-point spatially weighted k-NN method for object-based classification".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 52(2016):263-274.