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亚热带人工林高分影像GLCM纹理的应用策略研究

李蓉辉 陈玲 吴明晶 余小龙 赵秀海

李蓉辉, 陈玲, 吴明晶, 余小龙, 赵秀海. 亚热带人工林高分影像GLCM纹理的应用策略研究[J]. 北京林业大学学报, 2021, 43(1): 1-9. doi: 10.12171/j.1000-1522.20200139
引用本文: 李蓉辉, 陈玲, 吴明晶, 余小龙, 赵秀海. 亚热带人工林高分影像GLCM纹理的应用策略研究[J]. 北京林业大学学报, 2021, 43(1): 1-9. doi: 10.12171/j.1000-1522.20200139
Li Ronghui, Chen Ling, Wu Mingjing, Yu Xiaolong, Zhao Xiuhai. Application strategies for GLCM textures from very high spatial resolution optical imagery over subtropical plantations[J]. Journal of Beijing Forestry University, 2021, 43(1): 1-9. doi: 10.12171/j.1000-1522.20200139
Citation: Li Ronghui, Chen Ling, Wu Mingjing, Yu Xiaolong, Zhao Xiuhai. Application strategies for GLCM textures from very high spatial resolution optical imagery over subtropical plantations[J]. Journal of Beijing Forestry University, 2021, 43(1): 1-9. doi: 10.12171/j.1000-1522.20200139

亚热带人工林高分影像GLCM纹理的应用策略研究

doi: 10.12171/j.1000-1522.20200139
基金项目: 中央高校基本科研业务费专项(2019ZY24、TD2013-1),国家自然科学基金项目(41301357)
详细信息
    作者简介:

    李蓉辉。主要研究方向:林业遥感理论与应用研究。Email:rh1634541665@163.com 地址:100083 北京市清华东路35号北京林业大学林学院森林经理学科

    责任作者:

    陈玲,博士,副教授。主要研究方向:林业定量遥感。Email:chenling8247@126.com 地址:同上

  • 中图分类号: X17

Application strategies for GLCM textures from very high spatial resolution optical imagery over subtropical plantations

  • 摘要:   目的  探讨纹理变量及其相应参数配置范围,阐明各纹理变量随输入参数的变化规律,以便指导高分光学影像纹理在林业上的应用。  方法  以福建省将乐国有林场不同坡向不同龄组的杉木人工林为例,基于QuickBird影像的全色波段进行灰度共生矩阵(GLCM)纹理的计算与分析。  结果  结果表明:(1)除均值外的所有GLCM纹理变量对阴坡的3个龄组的区分能力均强于阳坡,且纹理变量优选需同时考虑衡量指标和纹理变量之间的相关程度。(2)窗口大小是统计组和有序组纹理最关键的输入参数,合适的窗口大小与影像的分辨率以及研究对象的空间尺度有关,对比度组纹理与窗口大小无关,可随意设置。(3)应用统计组和有序组纹理,无需关注像元间距,而应用对比度组纹理,不可忽视像元间距。(4)应用统计组纹理,像元间距越大越需关注计算方向;而对比度组和有序组纹理则相反,即像元间距越小越需关注计算方向。(5)作为最不受研究人员重视的灰度量化等级,推荐采用32或者64。  结论  高分光学影像的纹理信息对光谱重叠度较高的地物具有一定的区分能力,能部分“弥补”阴影导致的光谱信号损失,但在应用中需对纹理变量及其输入参数进行优化选择和配置。该文的研究结论能够为高分光学影像纹理信息的优化应用提供实用的参考借鉴。

     

  • 图  1  研究区QuickBird假彩色合成影像及踏查路线与样地分布

    Figure  1.  False color composite QuickBird image of the study area and the spatial distribution of survey routes and sample plots

    图  2  样本子区不同林龄杉木QuickBird全色波段影像

    A、B和C依次为阴坡的杉木幼龄林、中龄林和成熟林;D、E和F分别为阳坡的杉木幼龄林、中龄林和成熟林。A, B and C are young, intermediate and mature Chinese fir on shady slope; D, E and F are those on sunny slope.

    Figure  2.  QuickBird panchromatic images of subregions representing Chinese fir with different age classes under variedtopographic conditions

    图  3  GLCM纹理变量相关关系散点图

    Figure  3.  Scatter plots of correlations for GLCM texture variables

    图  4  阴坡优选纹理变量的J值排序为前5%的输入参数的直方图分布

    Figure  4.  Histogram of input parameters for optimal textural features with J ranking at the top 5% over shady slope

    图  5  阳坡优选纹理变量的J值排序为前5%的输入参数的直方图分布

    Figure  5.  Histogram of input parameters for optimal textural features with J ranking at the top 5% over sunny slope

    图  6  统计组纹理变量随输入参数的变化

    a、e、i:像元间距 = 1;b、f、j:像元间距 = 5;c、g、k:像元间距 = 9,d、h、l:像元间距 = 13。a, e, and i, displacement = 1; b, f, j, displacement = 5; c, g, k, displacement = 9; d, h, l, displacement =13.

    Figure  6.  Effects of input parameters on statistic group textures

    图  7  对比度组纹理变量随输入参数的变化

    a、e:像元间距 = 1;b、f:像元间距 = 5;c、g:像元间距 = 9,d、h:像元间距 = 13。a, e, displacement = 1; b, f, displacement = 5; c, g, displacement = 9; d, h, displacement = 13.

    Figure  7.  Effects of input parameters on contrast group textures

    图  8  有序组纹理变量随输入参数的变化

    a、c、e、g:像元间距 = 1;b、d、f、h:像元间距 = 13。a, c, e, g, displacement = 1; b, d, f, h, displacement = 13.

    Figure  8.  Effects of input parameters on orderliness group textures

    表  1  各GLCM纹理变量在阴坡和阳坡的最大J

    Table  1.   The maximum J values for each GLCM textural variable on shady slope and sunny slope

    GLCM 纹理变量
    GLCM textural variable
    统计组 Statistic group对比度组 Contrast group有序组 Orderliness group
    均值
    Mean
    方差
    Variance
    对比度
    Contrast
    差异性
    Dissimilarity
    均一性
    Homogeneity

    Entropy
    角二阶矩
    Angular second moment
    阴坡最大J
    The maximum J value for shady slope
    0.771.312.882.892.701.613.00
    阳坡最大J
    The maximum J value for sunny slope
    1.191.081.952.102.511.342.00
    下载: 导出CSV
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  • 收稿日期:  2020-05-11
  • 修回日期:  2020-06-16
  • 网络出版日期:  2020-12-14
  • 刊出日期:  2021-02-05

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