| 日光温室的纹理特征分层提取 |
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| 引用本文： | 李洪伟,刘勇.日光温室的纹理特征分层提取[J].测绘科学,2017,42(8). |
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| 作者姓名： | 李洪伟 刘勇 |
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| 作者单位： | 1. 兰州大学资源环境学院,兰州730000;68029部队,兰州 730000;2. 兰州大学资源环境学院,兰州,730000 |
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| 摘 要： | 针对高分辨率影像上日光温室的信息提取问题,该文提出了利用支持向量机、最近邻算法结合纹理特征在不同层上分别提取连片日光温室和独栋日光温室的方法。实验表明:纹理特征能提高分类精度,在大尺度的层上,分类精度提升幅度较大,但在小尺度的层上,分类精度提升幅度会比较小;并不是参与运算特征数越多,分类精度越高,多数情况下光谱+纹理组合的分类精度最高;提取连片日光温室的最优方案是支持向量机和光谱+形状+纹理(7像素×7像素),总精度为92.86%,Kappa系数为0.90,而提取独栋日光温室最优方案为SVM和光谱+纹理(11像素×11像素),总精度为88.39%,Kappa系数为0.86。
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| 关 键 词： | 支持向量机 最近邻法 多尺度纹理 日光温室 基于对象的影像分析 |
Extraction of greenhouse information using multiscale texture features |
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| LI Hongwei,LIU Yong.Extraction of greenhouse information using multiscale texture features[J].Science of Surveying and Mapping,2017,42(8). |
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| Authors: | LI Hongwei LIU Yong |
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| Abstract: | Aiming at the problem of extracting greenhouse information from high resolution image,in this article,a method using support vector machine (SVM) and nearest neighbor classifier with textural features extracted continuous and single greenhouses on multi-layers were proposed.The results showed that:texture feature could improve classification accuracy,which was improved more on layers with bigger scale and improved less on layers with smaller scale;classification accuracy was not always higher with more features,in most cases it would get better classification by using the combination of spectral and texture features;the best combination for extracting continuous greenhouse was SVM with regard to spectral,shape,texture features in the window size of 7pixel× 7pixel,overall accuracy and Kappa reached 92.85％ and 0.90,and the best combination for extracting single greenhouse was SVM with regard to spectral,shape,texture features in the window size of 11 pixel× 11 pixel,overall accuracy and Kappa reached 88.03 ％ and 0.85. |
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| Keywords: | SVM nearest neighbor classifier multiscale texture greenhouse object-based image analysis |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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