[1]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),2019,43(03):099-106.[doi:10.3969/ j.issn.1000-2006.201711035]
 TAO Huan,LI Cunjun*,XIE Chunchun,et al.Recognition of red-attack pine trees from UAV imagery based on the HSV threshold method[J].Journal of Nanjing Forestry University(Natural Science Edition),2019,43(03):099-106.[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
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《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

卷:
43
期数:
2019年03期
页码:
099-106
栏目:
研究论文
出版日期:
2019-05-15

文章信息/Info

Title:
Recognition of red-attack pine trees from UAV imagery based on the HSV threshold method
文章编号:
1000-2006(2019)03-0099-08
作者:
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
Author(s):
TAO Huan1 LI Cunjun1* 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
关键词:
松材线虫病 变色松树 无人机影像 HSV色彩模型 RGB色彩模型
Keywords:
pine wilt disease red attack pine trees unmanned aerial vehicle(UAV)imagery hue-saturation-value(HSV)model red-green-blue(RGB)model
分类号:
S771. 8
DOI:
10.3969/ j.issn.1000-2006.201711035
文献标志码:
A
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.

参考文献/References:

[1] VOLLENWEIDER P, GÜNTHARDT-GOERG M S. Diagnosis of abiotic and biotic stress factors using the visible symptoms in foliage[J]. Environmental Pollution, 2005, 137(3): 455-465. DOI:10.1016/j.envpol.2005.01.032.
[2] 陈元生, 黄燕洪, 周满生. 松材线虫病疫木伐桩除害处理技术概述[J]. 林业科技开发, 2014, 28(1): 12-14. DOI:10.13360/j.issn.1000-8101.2014.01.003.
CHEN Y S, HUANG Y H, ZHOU M S. A review on techniques of extinction treatment in diseased wood stumps caused by Bursaphelenchus xylophilus[J]. Journal of Forestry Engineering, 2014, 28(1): 12-14.
[3] 蒋丽雅, 盛常顺, 马圣安, 等. 松材线虫病疫木的微波除害处理技术[J]. 南京林业大学学报(自然科学版), 2006, 30(6): 87-90. DOI:10.3969/j.issn.1000-2006.2006.06.020.
JIANG L Y, SHENG C S, MA S A, et al. Study on processing timber infected with pine wood nematode using microwave[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2006, 30(6): 87-90.
[4] WULDER M A, DYMOND C C, WHITE J C, et al. Surveying mountain pine beetle damage of forests:a review of remote sensing opportunities[J]. Forest Ecology and Management, 2006, 221(1/3): 27-41. DOI:10.1016/j.foreco.2005.09.021.
[5] WULDERM A, WHITE J C, BENTZ B J, et al. Augmenting the existing survey hierarchy for mountain pine beetle red-attack damage with satellite remotely sensed data[J]. Forestry Chronicle, 2006, 82(2): 187-202. DOI:10.5558/tfc82187-2.
[6] 国家林业局. 松材线虫普查监测技术规程:GB / T 23478 — 2009[S]. 北京:中国标准出版社, 2009.
[7] 张衡, 潘洁, 巨云为, 等. 基于高光谱数据的马尾松松萎蔫病早期监测[J]. 东北林业大学学报, 2014, 42(11): 115-119. DOI:10.3969/j.issn.1000-5382.2014.11.026.
ZHANG H, PAN J, JU Y W, et al. Early detection of pine wilt disease in Pinus massoniana with hyperspectral data[J]. Journal of Northeast Forestry University, 2014, 42(11): 115-119.
[8] 黄明祥, 龚建华, 李顺, 等. 松材线虫病害高光谱时序与敏感特征研究[J]. 遥感技术与应用, 2012, 27(6): 954-960.
HUANG M X, GONG J H, LI S, et al. Study onpine wilt disease hyper-spectral time series and sensitive features[J]. Remote Sensing Technology and Application, 2012, 27(6): 954-960.
[9] 王震, 张晓丽, 安树杰. 松材线虫病危害的马尾松林木光谱特征分析[J]. 遥感技术与应用, 2007, 22(3): 367-370. DOI:10.3969/j.issn.1004-0323.2007.03.012.
WANG Z, ZHANG X L, AN S J. Spectral characteristics analysis of Pinus massoniana suffered by Bursaphelenchus xylophilus[J]. Remote Sensing Technology and Application, 2007, 22(3): 367-370.
[10] 张红梅, 陆亚刚. 无人机遥感技术国内松材线虫病监测研究综述[J]. 华东森林经理, 2017, 31(3): 29-32.
[11] FASSNACHT F E, LATIFI H, GHOSH A, et al. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality[J]. Remote Sensing of Environment, 2014, 140(1): 533-548. DOI:10.1016/j.rse.2013.09.014.
[12] 武红敢, 常原飞. 高新技术在林业有害生物普查中的应用前景分析[J]. 中国森林病虫, 2014, 33(5): 30-34. DOI:10.3969/j.issn.1671-0886.2014.05.009.
WU H G, CHANG Y F. Application prospects of high-tech in survey of forest pests[J]. Forest Pest and Disease, 2014, 33(5): 30-34.
[13] 乔睿, 唐娉, 石进, 等. WorldView-2影像的红叶松树识别研究[J]. 北京林业大学学报, 2015, 37(11): 33-40. DOI:10.13332/j.1000-1522.20150112.
QIAO R, TANG P, SHI J, et al. Recognition of red-attack pine trees using WorldView-2 imagery[J]. Journal of Beijing Forestry University, 2015, 37(11): 33-40.
[14] DENNISON P E, BRUNELLE A R, CARTER V A. Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data[J]. Remote Sensing of Environment, 2010, 114(11): 2431-2435. DOI:10.1016/j.rse.2010.05.018.
[15] HICKE J A, LOGAN J. Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery[J]. International Journal of Remote Sensing, 2009, 30(17): 4427-4441. DOI:10.1080/01431160802566439.
[16] COOPS N C, JOHNSON M, WULDER M A, et al. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation[J]. Remote Sensing of Environment, 2006, 103(1): 67-80. DOI:10.1016/j.rse.2006.03.012.
[17] QIN L, WANG X, JIANG J, et al. Use hyperspectral remote sensing technique to monitoring pine wood nomatode disease preliminary[C]//LIU W Q, WANG J N. Hyperspectral remote sensing applications and environmental monitoring and safety testing technology. Beijing: Society of Photo-Optical Instrumentation Engineers(SPIE), 2016.
[18] MEDDENS A J H, HICKE J A, VIERLING L A, et al. Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery[J]. Remote Sensing of Environment, 2013, 132(10): 49-58. DOI:10.1016/j.rse.2013.01.002.
[19] MEDDENS A J H, HICKE J A. Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA[J]. Forest Ecologyand Management, 2014, 322(3):78-88. DOI:10.1016/j.foreco.2014.02.037.
[20]WULDER M A, WHITE J C, COOPS N C, et al. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring[J]. Remote Sensing of Environment, 2008, 112(6):2729-2740. DOI: 10.1016/j.rse.2008.01.010.
[21] MEIGS G W, KENNEDY R E, COHEN W B. A landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests[J]. Remote Sensing of Environment, 2011, 115(12): 3707-3718. DOI:10.1016/j.rse.2011.09.009.
[22]WULDER M A, WHITE J C, BENTZ B, et al. Estimating the probability of mountain pine beetle red-attack damage[J]. Remote Sensing of Environment, 2006, 101(2):150-166. DOI: 10.1016/j.rse.2005.12.010.
[23]SKAKUN R S, WULDER M A, FRANKLIN S E. Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage[J]. Remote Sensing of Environment, 2003, 86(4):433-443. DOI: 10.1016/S0034-4257(03)00112-3.
[24]LEHMANN J R K, NIEBERDING F, PRINZ T, et al. Analysis of unmanned aerial system-based CIR images in forestry: a new perspective to monitor pest infestation levels[J]. Forests, 2015, 6(3):594-612. DOI: 10.3390/f6030594.
[25]PARK J K, KIM M G. Acquisition of geospatial information for forest management using unmanned aerial vehicle[J]. Advanced Science and Technology Letters, 2014, 62:78-82. DOI: 10.14257/astl.2014.62.20.
[26] NÄSI R, HONKAVAARA E, LYYTIKÄINEN-SAARENMAA P, et al. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level[J]. Remote Sensing, 2015, 7(11): 15467-15493. DOI:10.3390/rs71115467.
[27] 吕晓君, 王君, 喻卫国, 等. 无人机监测林业有害生物初探[J]. 湖北林业科技, 2016, 45(4):30-33. DOI:10.3969/j.issn.1004-3020.2016.04.008.
LÜ X J, WANG J, YU W G, et al. Study on monitoring forests pests and diseases by unmanned aerialvehicle[J]. Hubei Forestry Science and Technology, 2016, 45(4):30-33.
[28] 李卫正, 申世广, 何鹏, 等. 低成本小型无人机遥感定位病死木方法[J]. 林业科技开发, 2014, 28(6): 102-106.DOI:10.13360/j.issn.1000-8101.2014.06.025.
LI W Z, SHEN S G, HE P, et al. A precisely positioning technique by remote sensing the dead trees in stands with inexpensive small UAV[J]. Journal of Forestry Engineering, 2014, 28(6): 102-106.
[29] 胡根生, 张学敏, 梁栋,等. 基于加权支持向量数据描述的遥感图像病害松树识别[J]. 农业机械学报, 2013, 44(5):258-263. DOI:10.6041/j.issn.1000-1298.2013.05.045.
HU G S, ZHANG X M, LIANG D, et al. Infected pine recognition in remote sensing images based on weighted support vector data description[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013,44(5):258-263.
[30] 胡根生, 张学敏, 梁栋. 基于WWSVDD多分类的遥感图像病害松树识别[J]. 北京邮电大学学报, 2014, 37(2):23-27.DOI: 10.13190/j.jbupt.2014.02.006.
HU G S, ZHANG X M, LIANG D. Infected pine recognition in remote sensing images using WWSVDD multi-classification[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(2):23-27.
[31]PASHER J, KING D J. Mapping dead wood distribution in a temperate hardwood forest using high resolution airborne imagery[J]. Forest Ecology and Management, 2009, 258(7):1536-1548. DOI: 10.1016/j.foreco.2009.07.009.
[32] 吴琼. 基于遥感图像的松材线虫病区域检测算法研究[D]. 合肥:安徽大学, 2013.
WU Q. Research on Bursaphelenchus xylophilus area detection based on remote sensing image[D]. Hefei: Anhui University, 2013.
[33] 刘焕秀, 武海卫, 杨晓霞, 等. 黄岛区林业病虫害发生的现状、原因及防治措施探究[J]. 青海农林科技, 2017(2): 88-92. DOI:10.3969/j.issn.1004-9967.2017.02.024.
LIU H X, WU H W, YANG X X, et al. Current situation, causes and control measures of forest pests and diseases in Huangdao District[J]. Science and Technology of Qinghai Agriculture and Forestry, 2017(2): 88-92.
[34] CHENG H D, JIANG X H, SUN Y, et al. Color image segmentation: advances and prospects[J]. Pattern Recognition, 2001, 34(12): 2259-2281. DOI:10.1016/s0031-3203(00)00149-7.
[35] DONY R D, WESOLKOWSKI S. Edge detection on color images using RGB vector angles[C]//MENG M. Engineering solutions for the next millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering, Edmonton, 1999: 687-692.
[36] PEKEL J F, VANCUTSEM C, BASTIN L, et al. A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data[J]. Remote Sensing of Environment, 2014, 140: 704-716. DOI:10.1016/j.rse.2013.10.008.
[37] JAIN A K. Fundamentals of digital image processing[M]. New Jersey: Prentice Hall, 1989.
[38]葛宏立, 王鑫, 杜华强, 等. 无人机航片松材线虫病病死木的膨胀:剔除信息提取法: CN 101770577[P]. 2010-07-07.
[39] 黄昌狄, 徐芳. 基于真彩色高分辨遥感影像稀疏植被覆盖检测[J]. 测绘地理信息, 2016, 41(5):42-46. DOI:10.14188/j.2095-6045.2016.05.010.
HUANG C D, XU F. Extraction of sparse vegetation based on true color high resolution remote sensing image[J]. Journal of Geomatics, 2016, 41(5):42-46.
[40] PEKEL J F, CECCATO P, VANCUTSEM C, et al. Development and application of multi-temporal colorimetric transformation to monitor vegetation in the desert locust habitat[J]. IEEE Journal of Selected Topics in Applied Earth Observationsand Remote Sensing, 2011, 4(2): 318-326. DOI:10.1109/jstars.2010.2052591.
[41] 王成波. 面向松材线虫病监测的无人机影像变色松树提取与地面调查综合技术研究[D]. 北京: 中国科学院大学, 2015.
WANG C B. Study on automatic recognition of pinewood nematode infested pine trees on UVA images and services-oriented informationized field investigations[D].Beijing: University of Chinese Academy of Sciences, 2015.
[42] ORTIZ S M, BREIDENBACH J, KÄNDLER G. Early detection of bark beetle green attack using TerraSAR-X and RapidEye data[J]. Remote Sensing, 2013, 5(4):1912-1931.DOI:10.3390/rs5041912.
[43] EITEL J U H, VIERLING L A, LITVAK M E, et al. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland[J]. Remote Sensing of Environment, 2011, 115(12):3640-3646.DOI: 10.1016/j.rse.2011.09.002.
[44] 马菁, 刘维, 张晓丽. 遥感在松材线虫病早期监测预测上的研究进展[J]. 林业调查规划, 2011, 36(5): 75-80. DOI:10.3969/j.issn.1671-3168.2011.05.018.
MA J, LIU W, ZHANG X L. Remote sensing research on early monitoring and prediction of pine wilt disease[J]. Forest Inventory and Planning, 2011, 36(5): 75-80.
[45] SAFRANYIK L, CARROLL A L. The biology and epidemiology of the mountain pine beetle in lodgepole pine forests[C]// SAFRANYIK L, WILSON B. The mountain pine beetle: a synthesis of biology, management, and impacts on lodgepole pine. Victoria B C: Natural Resources Canada, Canadian Forest Service, 2006:123-154.

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 ZHAO Gui-hua,CHEN Xiao-yin,WU Yu-zhu,et al.Study on the Fungi from the Diseased Pine Wood Nematode[J].Journal of Nanjing Forestry University(Natural Science Edition),2006,30(03):079.[doi:10.3969/j.jssn.1000-2006.2006.02.019]
[7]王敏敏,叶建仁*,王云华.引诱剂防治松材线虫病及其配套技术[J].南京林业大学学报(自然科学版),2006,30(04):129.[doi:10.3969/j.jssn.1000-2006.2006.04.030]
 WANG Min-min,YE Jian-ren*,WANG Yun-hua.Aplication of Attractant for Controlling Pine Wilt Disease and Interrelated Techniques[J].Journal of Nanjing Forestry University(Natural Science Edition),2006,30(03):129.[doi:10.3969/j.jssn.1000-2006.2006.04.030]
[8]徐福元,葛明宏,汪企明,等.马尾松种源对松材线虫病的抗性[J].南京林业大学学报(自然科学版),1998,22(02):029.[doi:10.3969/j.jssn.1000-2006.1998.02.007]
 Xu Fuyuan Ge Minghong Wang Qiming Zhang Pei Zhu Kegong(Forestry Research Institute of Jiangsu Province Nanjing ) (Nanjing Forestry University ) Zhao Julin Xu Dong Wang Mingming(Agroforest Bureau of Nanjing).STUDIES ON THE MASSON PINE PROVENANCES RESISTANCE TOPINE WOOD NEMATODE (PWN) DISEASE IN CHINA[J].Journal of Nanjing Forestry University(Natural Science Edition),1998,22(03):029.[doi:10.3969/j.jssn.1000-2006.1998.02.007]
[9]来燕学,周岳松,周永平,等.触破式微胶囊剂在飞防松材线虫病中的应用[J].南京林业大学学报(自然科学版),2000,24(02):075.[doi:10.3969/j.jssn.1000-2006.2000.02.022]
 Lai Yanxue Zhou Yuesong ?Zhou Yongpin\ Qi Xinkang\ Wang Zufei(Forest and Plant Quarantine Station of Ningbo City\ Ningbo\ 000).Application of Contacted breaking Release Microcapsules by Aerial Spraying for Pine Wilt Disease Controlling[J].Journal of Nanjing Forestry University(Natural Science Edition),2000,24(03):075.[doi:10.3969/j.jssn.1000-2006.2000.02.022]
[10]郭道森,赵博光,李周直.松材线虫病致病机理的研究进展[J].南京林业大学学报(自然科学版),2000,24(04):064.[doi:10.3969/j.jssn.1000-2006.2000.04.019]
 GUO Dao sen,ZHAO Bo guang,LI Zhou zhi.The Pathogenic Mechanism of Pine Wilt Disease Caused by Bursaphelenchus xylophilus[J].Journal of Nanjing Forestry University(Natural Science Edition),2000,24(03):064.[doi:10.3969/j.jssn.1000-2006.2000.04.019]

备注/Memo

备注/Memo:
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)。
更新日期/Last Update: 2019-05-15