DOI: 10.1016/j.jag.2016.06.017
Scopus记录号: 2-s2.0-84997605094
论文题名: A multiple-point spatially weighted k-NN method for object-based classification
作者: Tang Y ; , Jing L ; , Li H ; , Atkinson P ; M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 263
结束页码: 274
语种: 英语
英文关键词: k-NN
; Multiple-point statistics
; Object-based classification
; Training image
Scopus关键词: accuracy assessment
; correlation
; geostatistics
; image classification
; mapping
; pixel
; spatial analysis
; support vector machine
; WorldView
; 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. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80039
Appears in Collections: 气候变化事实与影响
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作者单位: Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing, China; Faculty of Science and Technology, Engineering Building, Lancaster University, Lancaster, United Kingdom; School of Geography, Archaeology and Palaeoecology, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom; Geography and Environment, University of Southampton, Highfield, Southampton, United Kingdom
Recommended Citation:
Tang Y,, Jing L,, Li H,et al. A multiple-point spatially weighted k-NN method for object-based classification[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52