| 结合L0平滑和超像素的高空间遥感影像非监督分类 |
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| 引用本文： | 杨泽楠,黄亮,王枭轩,方留杨,宋晶.结合L0平滑和超像素的高空间遥感影像非监督分类[J].全球定位系统,2019,44(4):33-39. |
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| 作者姓名： | 杨泽楠 黄亮 王枭轩 方留杨 宋晶 |
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| 作者单位： | 1.昆明理工大学 国土资源工程学院,云南 昆明 650093 |
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| 摘 要： | 针对基于像元的非监督分类方法对高空间遥感影像分类时易形成“椒盐”噪声和产生大量错分、漏分的问题,提出了一种结合L0平滑和超像素的非监督分类方法．首先采用L0算法对高空间遥感影像进行平滑操作,减少大量图像噪声及冗余信息;然后采用简单的线性迭代聚类（SLIC）超像素方法处理平滑后图像,进一步抑制椒盐现象的同时降低处理复杂度,得到初始聚类图;最后采用K-means非监督分类方法得到最终分类结果图．为验证本文提出的方法,选取3景高空间遥感影像作为实验数据．试验结果表明,采用提出的方法能准确对地物分类,且总体精度分别达到了72.46％、77.55％和78.44％,Kappa系数分别达到0.788、0.779和0.779．提出方法能有效解决分类中存在的“椒盐”现象,可提高分类精度,对高空间遥感影像分类具有一定的参考价值．
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| 关 键 词： | 高空间遥感影像 L0平滑 超像素 非监督分类 分类精度 |
Unsupervised classification of high spatial remote sensing image combining L0 smoothing and superpixel |
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| Institution: | 1.Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China2.Surveying and Mapping Geo Informatics Technology Research? Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China3.Broad Vision Engineering Consultants, Kunming 650093, China |
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| Abstract: | Aiming at the problem that the unsupervised classification method is easy to form “salt and pepper” noise and generate many errors and missed points in the classification of high spatial remote sensing images, an unsupervised classification method of high spatial remote sensing image based on L0 smoothing and superpixel is proposed. Firstly, a L0 algorithm is instituted to smooth the high space remote sensing image and reduce a range of image noises and redundant information. Then, a super pixel method of SLIC (Simple Linear Iterative Clustering) is used for that further inhibiting the salt and pepper phenomenon while reducing the processing complexity, and the initial clustering map is obtained. Finally, the K-means unsupervised classification method is established to receive the final classification result image. Furthermore, three high spatial remote sensing images are selected as experimental data to verify the method proposed in this paper. The experimental results show that the proposed method can achieve accurate classification of features, and the overall accuracy is 72.46％, 77.55％ and 78.44％ respectively. The Kappa coefficients are 0.788, 0.779 and 0.779 respectively. The proposed method can effectively solve the phenomenon of “salt and pepper” in the classification,? improve the classification accuracy and have certain reference value for high spatial remote sensing image classification. |
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