| 面向对象的无人机遥感影像岩溶湿地植被遥感识别 |
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| 引用本文： | 耿仁方,付波霖,金双根,蔡江涛,耿万轩,娄佩卿. 面向对象的无人机遥感影像岩溶湿地植被遥感识别[J]. 测绘通报, 2020, 0(11): 13-18. DOI: 10.13474/j.cnki.11-2246.2020.0346 |
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| 作者姓名： | 耿仁方 付波霖 金双根 蔡江涛 耿万轩 娄佩卿 |
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| 作者单位： | 1. 南京信息工程大学遥感与测绘工程学院, 江苏 南京 210044;2. 桂林理工大学测绘地理信息学院, 广西 桂林 541004;3. 南京信息工程大学地理科学学院, 江苏 南京 210044 |
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| 基金项目： | 国家自然科学基金（41801071） |
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| 摘 要： | 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。
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| 关 键 词： | 面向对象 岩溶湿地 无人机影像 多尺度分割 特征选择 |
| 收稿时间： | 2020-01-23 |
| 修稿时间： | 2020-04-21 |
Object-based Karst wetland vegetation classification using UAV images |
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| GENG Renfang,FU Bolin,JIN Shuanggen,CAI Jiangtao,GENG Wanxuan,LOU Peiqing. Object-based Karst wetland vegetation classification using UAV images[J]. Bulletin of Surveying and Mapping, 2020, 0(11): 13-18. DOI: 10.13474/j.cnki.11-2246.2020.0346 |
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| Authors: | GENG Renfang FU Bolin JIN Shuanggen CAI Jiangtao GENG Wanxuan LOU Peiqing |
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| Affiliation: | 1. School of Remote Sensing&Geomatics Engineering, Nanjing University of Information Science&Technology, Nanjing 210044, China;2. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China;3. School of Geographic Information Science, Nanjing University of Information Science&Technology, Nanjing 210044, China |
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| Abstract: | 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. |
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| Keywords: | objected-based Karst wetland UAV images multi-resolution segmentation feature selection |
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