李洪伟;刘勇;

• 1:兰州大学资源环境学院
• 2:68029部队

摘要(Abstract)：

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。

关键词(KeyWords)： 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

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41271360)

作者(Author): 李洪伟;刘勇;

Email:

DOI: 10.16251/j.cnki.1009-2307.2017.08.024

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