A multiple-point spatially weighted k-NN classifier for remote sensing. Issue 18 (16th September 2016)

• Record Type: Journal Article
• Authors: Tang, Yunwei; Jing, Linhai; Atkinson, Peter M.; Li, Hui
• Is Part Of: International journal of remote sensing. Volume 37:Issue 18(2016)
• Page Start: 4441
• Page End: 4459
• Publication Date: 2016-09-16
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1214300
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: 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.