A multiple-point spatially weighted k-NN method for object-based classification
文献类型：期刊论文
| 作者 | Tang, Yunwei1; Jing, Linhai1; Li, Hui1; Atkinson, Peter M.1 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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