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    刘晓双, 李才文, 赵义兵. 基于高空间分辨率时间序列影像的对象级侵占林地图斑快速检测[J]. 北京林业大学学报, 2022, 44(11): 60-69. DOI: 10.12171/j.1000-1522.20210261
    引用本文: 刘晓双, 李才文, 赵义兵. 基于高空间分辨率时间序列影像的对象级侵占林地图斑快速检测[J]. 北京林业大学学报, 2022, 44(11): 60-69. DOI: 10.12171/j.1000-1522.20210261
    Liu Xiaoshuang, Li Caiwen, Zhao Yibing. Rapid detection of object-level invaded forest map patch based on high spatial resolution time series image[J]. Journal of Beijing Forestry University, 2022, 44(11): 60-69. DOI: 10.12171/j.1000-1522.20210261
    Citation: Liu Xiaoshuang, Li Caiwen, Zhao Yibing. Rapid detection of object-level invaded forest map patch based on high spatial resolution time series image[J]. Journal of Beijing Forestry University, 2022, 44(11): 60-69. DOI: 10.12171/j.1000-1522.20210261

    基于高空间分辨率时间序列影像的对象级侵占林地图斑快速检测

    Rapid detection of object-level invaded forest map patch based on high spatial resolution time series image

    • 摘要:
        目的  为掌握森林资源动态变化情况,及时、快速、准确地发现侵占林地地块,并解决主流遥感变化检测方法对数据源和时相一致性要求高、人工干预多、过程繁琐等应用瓶颈,采用一种基于高空间分辨率时间序列影像的多尺度对象级分割和变化提取方法,对主流方法的分类和检测两个过程进行了融合和简化。
        方法  以陕西省白水县为研究区,采用GF-1和ZY-3卫星数据源,将前后两期遥感影像波段拆分和重组形成时间序列影像,对时间序列影像进行多尺度面向对象的分割,通过分割结果的光谱变化值统计学抽样判断临界点并制定提取阈值,再利用NDVI变化值对结果进行优化。
        结果  以人工目视解译结果作为参照,该方法的检测精度达86.2%。在成功检出的侵占林地图斑中,形状吻合较好或基本吻合的图斑占48.8%。
        结论  该方法能够实现侵占林地图斑的快速检测,在检测效率、精度和适应性方面可满足大范围、多时相、混合数据源森林资源监测工作的实际应用需要。

       

      Abstract:
        Objective  In order to know the dynamic changes of forest resources, detect the infringement of forest land promptly, quickly and accurately, and solve the application bottlenecks of mainstream remote sensing change detection methods such as high requirements of consistency for data source and data time, too much manual interventions and complicated process, this research used a multi-scale object-level segmentation and change extraction method based on high spatial resolution time-series images to merge and simplify the two processes of classification and detection in mainstream methods.
        Method  Taking Baishui County in Shaanxi Province of northwestern China as the research area, using GF-1 and ZY-3 satellite data sources, the two remote sensing image bands were split and reorganized to form the time-series image, and the time-series image was segmented in a multi-scale object-oriented manner. The spectral change value of the segmentation result was statistically sampled to determine the critical point and establish the extraction threshold, and then the NDVI change value was used to optimize the result.
        Result  Taking the results of manual visual interpretation as a reference, the detection accuracy of this method was 86.2%. Among the classes that successfully detected invading forests, classes with good or almost consistent shapes accounted for 48.8%.
        Conclusion  This method can realize the rapid detection of invaded forest classes, and can meet the practical application needs of large-scale, multi-temporal, and mixed image data source forest resource monitoring in terms of detection efficiency, accuracy and adaptability.

       

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