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基于Landsat 8 OLI辅助的亚米级遥感影像树种识别

魏晶昱 毛学刚 方本煜 包晓建 许振宇

魏晶昱, 毛学刚, 方本煜, 包晓建, 许振宇. 基于Landsat 8 OLI辅助的亚米级遥感影像树种识别[J]. 北京林业大学学报, 2016, 38(11): 23-33. doi: 10.13332/j.1000-1522.20160054
引用本文: 魏晶昱, 毛学刚, 方本煜, 包晓建, 许振宇. 基于Landsat 8 OLI辅助的亚米级遥感影像树种识别[J]. 北京林业大学学报, 2016, 38(11): 23-33. doi: 10.13332/j.1000-1522.20160054
WEI Jing-yu, MAO Xue-gang, FANG Ben-yu, BAO Xiao-jian, XU Zhen-yu.. Submeter remote sensing image recognition of trees based on Landsat 8 OLI support.[J]. Journal of Beijing Forestry University, 2016, 38(11): 23-33. doi: 10.13332/j.1000-1522.20160054
Citation: WEI Jing-yu, MAO Xue-gang, FANG Ben-yu, BAO Xiao-jian, XU Zhen-yu.. Submeter remote sensing image recognition of trees based on Landsat 8 OLI support.[J]. Journal of Beijing Forestry University, 2016, 38(11): 23-33. doi: 10.13332/j.1000-1522.20160054

基于Landsat 8 OLI辅助的亚米级遥感影像树种识别

doi: 10.13332/j.1000-1522.20160054
基金项目: 

国家自然科学基金项目(31300533)、“十二五”国家科技支撑计划项目(2012AA102001)、东北林业大学大学生创新项目(201410225152)。

详细信息
    作者简介:

    魏晶昱。主要研究方向:遥感与地理信息系统。Email:copperwei@aliyun.com 地址:150040 黑龙江省哈尔滨市香坊区和兴路26号东北林业大学林学院。   责任作者: 毛学刚,博士,讲师。主要研究方向:遥感与地理信息系统。Email:maoxuegang@aliyun.com 地址:同上。

    魏晶昱。主要研究方向:遥感与地理信息系统。Email:copperwei@aliyun.com 地址:150040 黑龙江省哈尔滨市香坊区和兴路26号东北林业大学林学院。   责任作者: 毛学刚,博士,讲师。主要研究方向:遥感与地理信息系统。Email:maoxuegang@aliyun.com 地址:同上。

Submeter remote sensing image recognition of trees based on Landsat 8 OLI support.

  • 摘要: 为研究高空间分辨率遥感影像与多光谱遥感影像协同进行面向对象树种识别的有效性,本研究以QuickBird高空间分辨率(全色0.61 m)和Landsat 8 OLI(30 m)遥感影像为基础数据,在分类的过程中采用2种分割方案(有Landsat 8 OLI遥感影像辅助的QuickBird遥感影像分割和无Landsat 8 OLI遥感影像辅助的QuickBird遥感影像分割)进行多尺度分割,对2种分割方案进行比较。基于QuickBird遥感影像和Landsat 8 OLI遥感影像提取光谱、纹理、空间3方面68种分类特征,应用最邻近分类法和支持向量机分类方法进行面向对象树种分类,采用相同的分类系统、统一的分割尺度以及同一套验证样本,利用Kappa系数、总精度、生产者精度和用户精度4个评价指标进行精度评价。结果表明:单独使用QuickBird高空间分辨率遥感影像的分割结果优于QuickBird高空间分辨率遥感影像与Landsat 8 OLI遥感影像协同分割的结果,最优分割阈值与合并阈值分别为25和90。在最优分割结果的基础上,多光谱Landsat 8 OLI遥感影像与QuickBird高空间分辨率遥感影像协同进行面向对象分类,最邻近分类法和支持向量机分类方法的分类总精度分别为85.35%(Kappa=0.701 3)和88.12%(Kappa=0.853 6);单独使用QuickBird高空间分辨率遥感影像进行面向对象分类,2种方法的分类总精度分别为79.67%(Kappa=0.693 9)和83.33%(Kappa=0.792 5)。QuickBird遥感影像在Landsat 8 OLI遥感影像辅助下,分类结果的地物边界更加清晰,总体精度及主要树种识别精度均得到了提高。研究成果应用在实地森林调查与区划时可有效缩短调査时间、减少调查成本、降低劳动强度、提高成果质量。

     

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  • 收稿日期:  2016-02-23
  • 刊出日期:  2016-11-30

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