基于大面阵无人机多光谱遥感的水生植被精细分类研究
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1.中国地质大学(北京);2.航天数维高新技术股份有限公司;3.中国科学院空天信息创新研究院

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基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Classification of Aquatic Vegetation Species in a River Based on UAV-borne Large Array Multi-Spectral Camera
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Affiliation:

1.China University of Geosciences (Beijing);2.Aerospace ShuWei Tech Co., Ltd;3.Aerospace Information Research Institute, Chinese Academy of Sciences

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    水生植被是评估河流健康的重要指标,动态监测河流水生植被对于了解其状况至关重要。然而,传统的野外调查和卫星遥感在河流水生植被监测中都有明显局限。本研究采用无人机载高像素航天数维KP-6多光谱相机,获取河北省固安县境内的白沟河多光谱影像,并基于三种监督分类法对影像内白沟河的芦苇、荇菜和金鱼藻等水生植被物种进行分类,研究结果表明,最大似然法的精度最高,总体精度为92.8%,Kappa系数0.91。此外,还将高像素航天数维KP-6相机与多种高分辨率卫星和其他无人机载多光谱相机进行了比较,发现其在河流水生植被监测方面具有高空间分辨率、宽覆盖和灵活获取等优势。本研究可以为无人机载多光谱遥感在河流水生植被监测中的应用提供有益的参考。

    Abstract:

    Aquatic vegetation is an important indicator of river health, and it is important to carry out dynamic monitoring of river aquatic vegetation. However, both traditional field surveys and satellite remote sensing have obvious limitations in monitoring river aquatic vegetation. In this study, a UAV (Unmanned Aerial Vehicle)-borne high-pixel Aerospace Shuwei KP-6 multispectral camera was used to acquire multispectral images of the Baigou River in Gu'an County, Hebei Province. Aquatic vegetation species such as reed, Nymphoides Peltata, and Ceratophyllum demersum L. were classified in the Baigou River based on three kinds of supervised classifications, and the maximum-likelihood method was examined to have the highest precision, with an overall precision of 92.8% and the Kappa coefficient of 0.91. In addition, the high-pixel Aerospace Shuwei KP-6 camera was compared with a variety of high-resolution satellites and a number of UAV-borne multispectral cameras, and was found to have the advantages of high spatial resolution, wide coverage and flexible acquisition in river aquatic vegetation monitoring. The present study can provide a useful reference for the application of UAV-borne multispectral remote sensing in riverine aquatic vegetation monitoring.

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高敏,谢娅,李潇屹,董韬,陈玥,张方方,王胜蕾,申维,李俊生.基于大面阵无人机多光谱遥感的水生植被精细分类研究[J].南京信息工程大学学报,,():

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  • 收稿日期:2024-05-21
  • 最后修改日期:2024-07-07
  • 录用日期:2024-07-09
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