面向对象的无人机遥感影像岩溶湿地植被遥感识别

Object-based Karst wetland vegetation classification using UAV images

以广西桂林会仙喀斯特国家湿地公园为研究区,以无人机航摄影像为数据源,综合利用面向对象的影像分析技术、随机森林算法、阈值分类方法和Boruta全相关特征变量选择算法进行岩溶湿地植被的遥感识别。结果表明:针对不同特征变量对岩溶湿地遥感识别的贡献率而言,光谱特征(DOM>DSM)>纹理特征(DOM>DSM)>几何特征>上下文变量;两个航摄影像数据集的总体分类精度都在85%以上,Kappa系数也高于0.85。本文研究结果对基于高空间分辨率无人机可见光影像的岩溶湿地植被遥感识别在特征变量选择、分割参数选择及方法选择方面具有一定的借鉴意义。

This study aims to classify Karst wetland vegetation on Huixian National Wetland Park,located in Guilin,Guangxi province using object-based image analysis technique,random forest algorithm,image thresholding approach and Boruta all-related features selection algorithm based on UAV images.Results are as follows: the contribution of different feature variables is described as follows:spectral feature(DOM spectral>DSM spectral) >texture feature(DOM texture >DSM texture) >geometric feature>contextual feature;the overall classification accuracy of two UAV data sets is above 85 % as well as Kappa coefficient.This study provides insights into feature variable selection,segmentation parameter setting and classification method selection for karst wetland vegetation classification using high spatial resolution UAV RGB images.