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

Tang, Yunwei and Jing, Linhai and Li, Hui and Atkinson, Peter Michael (2016) A multiple-point spatially weighted k-NN method for object-based classification. International Journal of Applied Earth Observation and Geoinformation, 52. pp. 263-274. ISSN 0303-2434

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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.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Applied Earth Observation and Geoinformation
Additional Information:
This is the author’s version of a work that was accepted for publication in International Journal of Applied Earth Observation and Geoinformation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Applied Earth Observation and Geoinformation, 52, 2016 DOI: 10.1016/j.jag.2016.06.017
Uncontrolled Keywords:
?? multiple-point statisticsk-nnobject-based classificationtraining imageglobal and planetary changeearth-surface processescomputers in earth sciencesmanagement, monitoring, policy and law ??
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Deposited On:
26 Jun 2018 12:46
Last Modified:
16 Jul 2024 23:55