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遥感技术与应用  2021, Vol. 36 Issue (4): 926-935    DOI: 10.11873/j.issn.1004-0323.2021.4.0926
遥感应用     
基于无人机高光谱影像的三江源草种精细识别研究
胡宜娜1(),安如1(),艾泽天2,都伟冰3
1.河海大学 水文水资源学院,江苏 南京 211100
2.滁州学院 地理信息与旅游学院,安徽 滁州 239000
3.河南理工大学 测绘与国土信息工程学院,河南 焦作 454003
Researches on Grass Species Fine Identification based on UAV Hyperspectral Images in Three-River Source Region
Yina Hu1(),Ru An1(),Zetian Ai2,Weibing Du3
1.College of Hydrology and Water Resources,Hohai University,Nanjing 211100,China
2.School of Geographic Information and tourism,Chuzhou University,Chuzhou 239000,China
3.School of Surveying and mapping and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China
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摘要:

草种精细识别对三江源区草地生态系统退化监测具有重要意义。基于无人机高光谱遥感系统,获取三江源草地退化典型区的高光谱影像。在对原始光谱特征利用XGBoost进行优化选择的基础上,结合扩展形态学属性剖面特征,利用稀疏多项式逻辑回归与自适应稀疏表示两种分类方法分别对影像上的不同可食与毒杂草种进行精细识别,在此基础上提出形状自适应的后处理方法对识别结果进行平滑处理。结果表明:①利用XGBoost方法选择出重要性高的光谱特征能提升高光谱数据的识别效果并节省运行时间;②利用空间—光谱特征的识别方法相较于仅利用光谱特征的方法可以有效改善草种识别效果,使总体精度提升4%~5%;③利用两种稀疏表示方法在小样本的情况下对草种精细识别的精度分别达到94.07%、93.15%,利用形状自适应后处理方法能有效提高多种毒杂草种的识别精度,使得总体精度分别提升约1.64%和1.12%。基于特征挖掘的稀疏表示分类方法能实现高精度的无人机高光谱影像草种精细识别,为更大范围的草原物种精细识别提供了技术支撑。

关键词: 无人机高光谱影像草种精细识别特征挖掘形状自适应稀疏表示三江源    
Abstract:

Fine identification of grass species is of great significance for grassland ecosystem degradation monitoring in the Three Rivers Source Region. Based on the UAV hyperspectral remote sensing system, the hyperspectral image of the typical grassland degradation area of Three-River Source Region was obtained. Firstly, using the obtained UAV hyperspectral image, the optimal bands combination were selected using XGBoost, the extended morphological attribute profile features were extracted and were combined with the selected spectral features. Secondly, sparse multinomial logistic regression and adaptive sparse representation methods were adopted to identify different grass species. Finally the shape adaptive based post-processing method was proposed to smooth the identification results. The results showed that: (1) Using the XGBoost method to select important spectral features can improve the identification result and save running time; (2) the spatial-spectral feature based method can effectively improve the identification result of grass species and the overall accuracy were improved by 4%~5% compared with the method of using only spectral features; (3) using two sparse representation methods,the overall accuracy of fine identification of grass species in the case of limited samples was 94.07% and 93.15% respectively, and the identification accuracy of various poison weed species was improved effectively by using shape adaptive post-processing method, which improved the overall accuracy by about 1.64% and 1.12%, respectively. The feature mining based sparse representation classification methods can achieve high-precision grass species fine identification of UAV hyperspectral images, and provide technical support for a wider range of grassland species fine identification.

Key words: UAV Hyperspectral Images    Grass species fine identification    Feature mining    Shape adaptive    Sparse representation    Three-River Source
收稿日期: 2020-04-30 出版日期: 2021-09-26
ZTFLH:  P237  
基金资助: 国家自然科学基金项目(41871326)
通讯作者: 安如     E-mail: yinahu7@163.com;anrunj@163.com
作者简介: 胡宜娜(1995-),女,河南信阳人,硕士研究生, 主要从事高光谱影像分类研究。E?mail: yinahu7@163.com
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引用本文:

胡宜娜,安如,艾泽天,都伟冰. 基于无人机高光谱影像的三江源草种精细识别研究[J]. 遥感技术与应用, 2021, 36(4): 926-935.

Yina Hu,Ru An,Zetian Ai,Weibing Du. Researches on Grass Species Fine Identification based on UAV Hyperspectral Images in Three-River Source Region. Remote Sensing Technology and Application, 2021, 36(4): 926-935.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.4.0926        http://www.rsta.ac.cn/CN/Y2021/V36/I4/926

图1  研究区地理位置与无人机高光谱影像(a) 研究区地理位置 (b) 无人机高光谱假彩色影像
图2  现场样方布设图与草种照片
图3  样本分布图
图4  不同草种的光谱曲线
图5  研究方法技术路线图
图6  形状自适应邻域数量与MMSAD散点图
图7  不同波段数的重要性
图8  不同波段数的识别精度与运行时间
图9  基于SMLR与ASR的草地识别结果
类别

训练样本/

验证样本

SMLR-EMAPSMLR-EMAP-SAASR-EMAPASR-EMAP-SA
PAUAPAUAPAUAPAUA
矮火绒8/830.891 60.840 91.000 00.846 90.879 50.973 30.963 90.833 3
棘豆17/1630.975 50.649 01.000 00.795 11.000 00.610 51.000 00.871 7
苔藓18/1680.982 10.896 70.994 00.933 00.988 10.873 70.928 60.725 6
细叶亚菊7/690.579 70.975 60.768 10.688 30.724 60.781 30.811 60.965 5
苔草19/1820.978 00.983 40.923 11.000 00.989 00.923 10.972 51.000 0
藏嵩草11/1 1111.000 00.848 71.000 00.886 01.000 00.952 81.000 00.776 9
小嵩草3/390.615 41.000 00.666 71.000 00.512 81.000 00.384 60.882 4
针茅3/330.636 41.000 00.666 71.000 00.636 41.000 00.424 20.823 5
裸地15/1 6580.956 60.995 60.968 60.999 40.935 50.995 50.959 60.995 0
OA0.940 70.957 10.931 50.942 7
Kappa0.893 30.922 50.878 50.896 7
平均用时/s111152646692
表1  基于SMLR与ASR的草地识别精度
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