基于高分二号遥感影像的树种分类方法

doi:10.11873/j.issn.1004-0323.2019.5.0970

遥感技术与应用

2019, Vol. 34

Issue (5): 970-982 DOI: 10.11873/j.issn.1004-0323.2019.5.0970

林业遥感专栏

基于高分二号遥感影像的树种分类方法

李哲,张沁雨,彭道黎(

)

北京林业大学大学林学院，北京 100083

Classification Method of Tree Species based on GF-2 Remote Sensing Images

Zhe Li,Qinyu Zhang,Daoli Peng(

)

College of Forestry, Beijing Forestry University, Beijing 100083, China

全文: PDF(5608 KB) HTML

摘要：

为推广国产高分数据在森林树种分类方面的应用，以北京市延庆区八达岭国家森林公园主要区域的6期高分二号影像为数据源，在分层分类的基础上，利用支持向量机递归特征消除、C5.0决策树、FSO 3种特征优选方法，从4种特征维度下实现面向对象的支持向量机和随机森林的森林树种分类，最终取得总体精度平均为83.65%，特定树种生产者精度介于93.75%（山杏）和38.10%（刺槐）之间，特定树种用户精度介于100%（华北落叶松）和44.74%（榆树）之间的良好结果。结果表明：C5.0决策树耗时最短（0.01 h）且其所选特征应用于分类总体精度最高（86.90%）；在不同特征维度下支持向量机分类的总体精度比随机森林平均高出3.28%；支持向量机和随机森林均对特征维度不敏感，但良好的特征优选结果仍会对支持向量机的分类效率（最高提升86.98%）和随机森林的分类精度（最高提升9.22%）产生较大影响。

关键词： 高分二号; 树种分类; 特征优选; 支持向量机; 随机森林

Abstract:

In order to promote the application of Chinese Gaofen data in the classification of forest tree species, The six GF-2 images of the main area of Badaling National Forest Park in Yanqing District, Beijing were used as the data source, we used support vector machine-recursive feature elimination, C5.0 decision tree and feature space optimization three feature optimization methods to accomplish the object-oriented Support Vector Machines (SVM) and Random Forest (RF) forest tree classification from four feature dimensions on the basis of the hierarchical classification. we can achieve good classification results that the average Overall Accuracy of the study was 83.65%, the Producer's Accuracy of specific tree species was between 93.75% (Apricot) and 38.10% (Locust), and the Use's Accuracy of specific tree species was between 100% (North China Larch) and 44.74% (Elm). The results showed the C5.0 feature selection took the shortest time(0.01 h) and features selected by it could be applied to the highest classification accuracy (86.90%). Under different feature dimensions, the Overall Accuracy of SVM classification was 3.28% higher than the RF.SVM and RF were both insensitive to feature dimensions, but good feature optimization results will still have a large impact on the classification efficiency of SVM(Highest improvement was 86.98%) and the classification accuracy of RF(Highest improvement was 9.22%).

Key words: GF-2 Tree species classification Feature selection Support vector machines Random forest

收稿日期: 2018-10-13 出版日期: 2019-12-05

ZTFLH:

TP75

基金资助: 国家林业局948项目(2015?4?32)

通讯作者: 彭道黎 E-mail: dlpeng@bjfu.edu.cn

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引用本文:

李哲,张沁雨,彭道黎. 基于高分二号遥感影像的树种分类方法[J]. 遥感技术与应用, 2019, 34(5): 970-982.

Zhe Li,Qinyu Zhang,Daoli Peng. Classification Method of Tree Species based on GF-2 Remote Sensing Images. Remote Sensing Technology and Application, 2019, 34(5): 970-982.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.0970 或 http://www.rsta.ac.cn/CN/Y2019/V34/I5/970

图1 研究区域位置与外业调查点分布图（a）研究区域位置（b） 2018年4月25日获取的真彩色影像

表 1 影像信息统计

表 2 特征统计

图2 尺度参数分析结果（150~350）

图3 尺度参数分析结果（80~150）

表 3 第二层分类优选特征及重要性排名

表 4 优选特征及重要性排名（C5.0）

表 5 优选特征及重要性排名（SVM-RFE）

表 6 优选特征及重要性排名（FSO）

表 7 乔木林地与灌木林地混淆矩阵

表8 不同特征维度下SVM的精度比较

图 4 Mtry对袋外误差的影响

表 9 不同特征维度下RF的精度比较

图5 分类结果比较

图6 总体精度比较

图 7 特征重要性比较

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