Sentinel-2A数据支持下的雷州半岛植被类型识别

本文以雷州半岛为研究区,利用Sentinel-2A影像数据和真实植被样本数据,综合探讨了机器学习中随机森林与支持向量机的分类效果,并与传统的最大似然法进行比较。提取Sentinel-2A影像9个波段、7个植被指数、72个纹理特征,通过递归特征消除法挑选了10个特征组合,并将其应用于3种分类方法中,对其分类效果进行比较。结果表明:①有效使用多种特征变量是提高植被类型识别精度的关键,就不同特征对植被类型识别的重要性而言,光谱特征与纹理特征相当且大于植被指数,三者重要性相差不大;②随机森林分类效果最佳,不但能对特征进行有效选择,而且能保证植被类型提取精度,提高运行效率;③基于随机森林特征选择的递归特征消除法得到的特征组合不能对其他分类器性能进行优化,对随机森林模型本身的优化效果也有限。

Recognition of vegetation types in Leizhou Peninsula based on Sentinel-2A data

Using Sentinel-2A image data and real vegetation sample data from Leizhou Peninsula as the research area,this paper comprehensively discusses the classification effects of random forest and support vector machine in machine learning,and compares them with the traditional maximum likelihood method.Firstly,9 bands,7 vegetation indexes and 72 texture features of Sentinel-2A image are extracted successfully,then the feature combination of 10 features is selected by recursive feature elimination method and applies to three classification methods,and the classification effect is compared.The results show that:①Effectively using a variety of characteristic variables is the key to improve the vegetation type recognition accuracy,in terms of the importance of the different characteristics of vegetation type recognition,the spectral features are the same to the texture features and greater than vegetation index,three importance are similar.②Random forest classification has the best effect,which can not only select features effectively,but also ensure the precision of vegetation type extraction and improve the operation efficiency.③The feature combination based on the recursive feature elimination method of random forest feature selection can not optimize the performance of other classifiers,and the optimization effect of the random forest model itself is limited.