Spectral–Spatial Hyperspectral Image Classification Based on KNN

被引：53
作者：
Huang K. [1 ]
Li S. [1 ]
Kang X. [1 ]
Fang L. [1 ]
机构：
[1] College of Electrical and Information Engineering, Hunan University, Changsha
来源：
Sensing and Imaging | 2016年 / 17卷 / 01期
基金：
中国国家自然科学基金;
关键词：
K nearest neighbor; Optimization; Spectral–spatial hyperspectral image classification; Support vector machines;
D O I：
10.1007/s11220-015-0126-z
中图分类号：
学科分类号：
摘要：
Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification. In this paper, a novel spectral–spatial hyperspectral image classification method based on K nearest neighbor (KNN) is proposed, which consists of the following steps. First, the support vector machine is adopted to obtain the initial classification probability maps which reflect the probability that each hyperspectral pixel belongs to different classes. Then, the obtained pixel-wise probability maps are refined with the proposed KNN filtering algorithm that is based on matching and averaging nonlocal neighborhoods. The proposed method does not need sophisticated segmentation and optimization strategies while still being able to make full use of the nonlocal principle of real images by using KNN, and thus, providing competitive classification with fast computation. Experiments performed on two real hyperspectral data sets show that the classification results obtained by the proposed method are comparable to several recently proposed hyperspectral image classification methods. © 2015, Springer Science+Business Media New York.
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页码：1 / 13
页数：12
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