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

Tang, Yunwei, Jing, Linhai, Atkinson, Peter M., Li, Hui
International journal of remote sensing 2016 v.37 no.18 pp. 4441-4459
land cover, models, remote sensing, statistics, support vector machines, urban areas, China
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 (MP k -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 MP k -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.