An improved k-NN method based on multiple-point statistics for classification of high-spatial resolution imagery

9th Symposium of the International Society for Digital Earth

地球科学;计算机科学

Tang, Y.^1 ; Jing, L.^1 ; Li, H.^1 ; Liu, Q.^1 ; Ding, H.^1

Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing

100094, China^1

关键词: Classification accuracy;    Decision tree classifiers;    High spatial resolution imagery;    K nearest neighbours (k-NN);    Multiple-point statistics;    Object-based classifications;    Remotely sensed images;    Support vector machine classifiers;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/34/1/012038/pdf
DOI  :  10.1088/1755-1315/34/1/012038

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In this paper, the potential of multiple-point statistics (MPS) for object-based classification is explored using a modified k-nearest neighbour (k-NN) classification method (MPk-NN). The method first utilises a training image derived from a classified map to characterise the spatial correlation between multiple points of land cover classes, overcoming the limitations of two-point geostatistical methods, and then the spatial information in the form of multiple-point probability is incorporated into the k-NN classifier. The remotely sensed image of an IKONOS subscene of the Beijing urban area was selected to evaluate the method. The image was object-based classified using the MPk-NN method and several alternatives, including the traditional 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 MPk-NN approach can achieve greater classification accuracy relative to the alternatives, which are 82.05% and 89.12% based on pixel and object testing data, respectively. Thus, the proposed method is appropriate for object-based classification.