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机载与星载高分遥感影像单木树冠分割方法和适宜性对比

孙振峰 张晓丽 李霓雯

孙振峰, 张晓丽, 李霓雯. 机载与星载高分遥感影像单木树冠分割方法和适宜性对比[J]. 北京林业大学学报, 2019, 41(11): 66-75. doi: 10.13332/j.1000-1522.20180446
引用本文: 孙振峰, 张晓丽, 李霓雯. 机载与星载高分遥感影像单木树冠分割方法和适宜性对比[J]. 北京林业大学学报, 2019, 41(11): 66-75. doi: 10.13332/j.1000-1522.20180446
Sun Zhenfeng, Zhang Xiaoli, Li Niwen. Comparison of individual tree crown extraction method and suitability of airborne and spaceborne high-resolution remote sensing images[J]. Journal of Beijing Forestry University, 2019, 41(11): 66-75. doi: 10.13332/j.1000-1522.20180446
Citation: Sun Zhenfeng, Zhang Xiaoli, Li Niwen. Comparison of individual tree crown extraction method and suitability of airborne and spaceborne high-resolution remote sensing images[J]. Journal of Beijing Forestry University, 2019, 41(11): 66-75. doi: 10.13332/j.1000-1522.20180446

机载与星载高分遥感影像单木树冠分割方法和适宜性对比

doi: 10.13332/j.1000-1522.20180446
基金项目: 国家重点研发计划子课题(2017YFD0600902)
详细信息
    作者简介:

    孙振峰。主要研究方向:资源监测与信息化管理。Email:sunzhenfeng_10@163.com  地址:100083 北京市海淀区清华东路35号北京林业大学林学院

    责任作者:

    张晓丽,教授,博士生导师。主要研究方向:定量遥感。Email:zhang-xl@263.net  地址:同上

  • 中图分类号: S771.8

Comparison of individual tree crown extraction method and suitability of airborne and spaceborne high-resolution remote sensing images

  • 摘要: 目的应用高分辨率遥感影像快速准确提取单木树冠信息,对现代森林管理具有重要意义。面向对象的多尺度分割方法能有效地解决基于像元特征分析的局限,是单木树冠提取的重要技术途径。本文对比分析了不同遥感平台和人工林树种的树冠提取精度,探究实验方法针对不同尺度影像数据和树种的优势及适用性,并结合调查目的为影像数据的选取提供参考。方法以广西壮族自治区高峰林场为研究区,选取低空无人机CCD、机载CCD和星载高分二号遥感影像数据,针对树冠区域与背景区域的对比度效果不佳的问题,首先采用小波变换进行图像增强处理,去除影像噪声,增强树冠与背景的对比度;然后应用面向对象的多尺度分割方法,排除背景区域的干扰,针对树冠区域进行单木树冠的快速提取;最后对3种影像下提取的杉木和桉树人工林单木树冠的流程和方法,以及树冠提取精度进行研究分析。结果采用小波变换对无人机和机载平台影像增强效果显著,无人机平台下桉树和杉木实验区单木分割精度分别为87%和93.3%,冠幅估测精度为84.2%和85.1%;机载平台下桉树和杉木实验区单木分割精度为89%和91.1%,冠幅估测精度为83.9%和84.4%;而小波变换对星载平台影像增强效果不佳,桉树和杉木实验区的单木分割精度为82%和89%,冠幅估测精度为72.3%和73.3%。结论在无人机和机载平台下,应用多尺度分割得到的树冠提取精度相接近;在星载平台下,直接应用多尺度分割进行单木树冠提取,受影像自身空间分辨率的局限,提取精度低于前两种平台,但也能够满足森林调查的基本需求。

     

  • 图  1  研究区示意图

    Figure  1.  Location of the study area

    图  2  单木树冠提取技术路线

    Figure  2.  Technical diagram for extracting tree crown

    图  3  小波分解示意图

    LL. 近似信息;HL. 水平细节信息;LH. 垂直细节信息;HH. 对角线细节信息。LL represents approximate information; HL represents horizontal detail information; LH represents vertical detail information; HH represents diagonal detail information.

    Figure  3.  Wavelet decomposition diagram

    图  4  原始影像及不同小波阈值处理后影像

    Figure  4.  Original image and different wavelet threshold processed images

    图  5  不同平台影像小波增强变换效果

    Figure  5.  Wavelet enhancement transform effects for different platform images

    图  6  影像树冠区域及分割结果

    Figure  6.  Forest area and segmentation results

    表  1  统计特征

    Table  1.   Statistical characteristics

    树种 Species影像 Image小波阈值系数
    Wavelet threshold coefficient
    边缘强度
    Edge strength
    均值
    Mean
    标准差
    Standard deviation
    杉木
    Cunninghamia lanceolata
    机载原始影像 Airborne original image 237 34.19 42.17 34.44
    增强后影像 Enhancement image 70.83 78.85 68.19
    桉树
    Eucalyptus robusta
    机载原始影像 Airborne original image 306 23.88 39.69 38.91
    增强后影像 Enhancement image 85.20 65.42 70.47
    杉木
    Cunninghamia lanceolata
    无人机原始影像 UAV original image 280 47.66 53.76 49.02
    增强后影像 Enhancement image 90.91 95.94 99.49
    桉树
    Eucalyptus robusta
    无人机原始影像 UAV original image 500 36.38 64.19 64.89
    增强后影像 Enhancement image 102.78 102.77 118.79
    杉木
    Cunninghamia lanceolata
    GF-2原始影像 GF-2 original image 1 050 150.35 131.40 103.17
    增强后影像 Enhancement image 118.15 133.01 98.74
    桉树
    Eucalyptus robusta
    GF-2原始影像 GF-2 original image 1 070 169.32 120.19 116.64
    增强后影像 Enhancement image 151.76 122.49 106.67
    下载: 导出CSV

    表  2  面向对象树冠分割参数

    Table  2.   Object-oriented tree crown segmentation parameters

    项目
    Item
    分割参数
    Segmentation parameter
    机载影像 Airborne image无人机影像 UAV imageGF-2影像 GF-2 image
    杉木Cunninghamia lanceolata桉树Eucalyptus robusta杉木Cunninghamia lanceolata桉树Eucalyptus robusta杉木Cunninghamia lanceolata桉树Eucalyptus robusta
    阈值分类
    Threshold classification
    灰度阈值 Gray threshold > 70 > 82 > 105 > 125 > 35 > 85
    单木树冠分割
    Individual tree crown segmentation
    分割尺度 Segmentation scale 18 12 120 130 4 4
    形状 Shape 0.9 0.9 0.9 0.9 0.9 0.9
    紧致度 Compactness 0.5 0.9 0.9 0.8 0.7 0.8
    下载: 导出CSV

    表  3  单木树冠提取精度分析

    Table  3.   Accuracy analysis on extracted individual tree crown

    项目
    Item
    树种
    Species
    树木总株树
    Total number
    of trees
    正确分割株数
    Correct segmentation
    number of trees
    单木分割精度
    Tree crown segmentation accuracy/%
    相对误差均值
    Relative error mean/%
    冠幅估测总精度
    Tree crown estimation accuracy/%
    机载影像
    Airborne image
    杉木 Cunninghamia lanceolata 45 41 91.1 15.6 84.4
    桉树 Eucalyptus robusta 45 40 89.0 16.1 83.9
    无人机影像
    UAV image
    杉木 Cunninghamia lanceolata 45 42 93.3 14.9 85.1
    桉树 Eucalyptus robusta 45 39 87.0 15.8 84.2
    GF-2影像
    GF-2 image
    杉木 Cunninghamia lanceolata 45 40 89.0 26.7 73.3
    桉树 Eucalyptus robusta 45 37 82.0 27.7 72.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-12-31
  • 修回日期:  2019-03-01
  • 网络出版日期:  2019-09-20
  • 刊出日期:  2019-11-01

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