基于HSV阈值法的无人机影像变色松树识别

doi:10.3969/j.issn.1000-2006.201711035

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南京林业大学学报（自然科学版） ›› 2019, Vol. 62 ›› Issue (03): 99-106.doi: 10.3969/j.issn.1000-2006.201711035

基于HSV阈值法的无人机影像变色松树识别

陶欢¹,李存军^1*,谢春春²,周静平¹,淮贺举¹,蒋丽雅³,李凤涛²

1.北京农业信息技术研究中心, 北京 100097; 2.山东瑞达有害生物防控有限公司, 山东济南 250101; 3.安徽省林业有害生物防治检疫局, 安徽合肥 230031

出版日期:2019-05-15 发布日期:2019-05-15
基金资助:
收稿日期:2017-11-13 修回日期:2018-04-30
基金项目:国家自然科学基金项目(41571423)。
第一作者:陶欢(taoh.11s@igsnrr.ac.cn),博士生。*通信作者:李存军(licj@nercita.org.cn), 研究员,ORCID(0000-0002-8485-6871)。

Recognition of red-attack pine trees from UAV imagery based on the HSV threshold method

TAO Huan¹, LI Cunjun^1*, XIE Chunchun², ZHOU Jingping¹, HUAI Heju¹, JIANG Liya³, LI Fengtao²

1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 2. Shandong Ruida Harmful Organism Control and Prevention Co., Ltd., Ji’nan 250101, China; 3. Bureau of Anhui Province for Forest Pests Control and Quarantine, Hefei 230031, China

Online:2019-05-15 Published:2019-05-15

摘要/Abstract

摘要： 【目的】提出一种色调-饱和度-明度(HSV)阈值划分方法,提高变色松树的调查效率,为疫木的砍伐提供数据支撑。【方法】基于变色松树与其他地类在“H-V空间”上的差异建立变色松树阈值提取规则; 对比分析HSV阈值法和红-绿-蓝(RGB)阈值法在不同情景下提取得到的变色松树识别结果,并对识别结果的精度进行评价。【结果】① 变色松树样本在“H-V空间”散点云图中有明显的聚类现象,而在“G-R空间”散点云图中呈条带状分布。② RGB色彩空间中的R和G之间存在较强的相关性,直接采用阈值法提取变色松树时漏分误差较大。HSV阈值法由于在色彩变换过程中能够分离出色调值H和亮度值V,便于进行阈值划分,对基于无人机数据的变色松树识别的总体精度要优于RGB阈值法。③ HSV阈值法对变色松树的识别适合于病死松树发展的后期监测,在对借助高分辨率影像提取的发病前松树分布进行掩膜后,可实现60%～65%的变色松树提取精度。【结论】HSV阈值法对于基于无人机影像的变色松树监测具有一定的优势,能提高人工判读的效率,可为基于无人机影像的变色松树监测提供理论和方法支撑。

Abstract: 【Objective】A hue-saturation-value(HSV)threshold method was used for the recognition of red attack pine trees from unmanned aerial vehicle(UAV)imagery. To improve the investigation efficiency of red-attack damage and provide basic data for sanitation logging. 【Method】The extraction rules of the HSV threshold method were built according to the difference between red attack pine trees and other elements in the “H-V space”. The recognition results of red attack pine trees between the RGB threshold method and HSV threshold method were compared under different scenarios. Next, the evaluation accuracy of both methods was calculated. 【Result】① Samples of red attack pine trees plotted in the “H-V space” point cloud showed significant clustering and a banding distribution in the “G-R space” point cloud. ② A strong correlation among the components in the RGB color space was a main factor for the large omission errors of red attack pine tree extraction using the RGB threshold method. The overall accuracy of the HSV threshold method, based on decoupling of the hue and value from the HSV color space and its convenient threshold segmentation, outperformed that of the RGB threshold method for red attack pine tree recognition based on UAV imagery. ③ The HSV threshold method performed well for monitoring pines affected by pine wilt disease in a late stage, which achieved 60%-65% overall accuracy when the distribution of pine trees before infection from high-resolution imagery was used as a mask. 【Conclusion】 The HSV threshold method shows some advantages for monitoring red attack pine trees based on UAV imagery compared to manual interpretation. This method provides theoretical and methodological support for the recognition of red attack pine trees from UAV imagery.

中图分类号:

S771. 8

陶欢,李存军,谢春春,等. 基于HSV阈值法的无人机影像变色松树识别[J]. 南京林业大学学报（自然科学版）, 2019, 62(03): 99-106.

TAO Huan, LI Cunjun, XIE Chunchun, ZHOU Jingping, HUAI Heju, JIANG Liya, LI Fengtao. Recognition of red-attack pine trees from UAV imagery based on the HSV threshold method[J].Journal of Nanjing Forestry University (Natural Science Edition), 2019, 62(03): 99-106.DOI: 10.3969/j.issn.1000-2006.201711035.

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基于HSV阈值法的无人机影像变色松树识别

Recognition of red-attack pine trees from UAV imagery based on the HSV threshold method

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