无人机可见光遥感影像的空间分辨率对毛竹立竹度识别精度的影响

Effect of Spatial Resolution of Unmanned Aerial Vehicle (UAV) Visible Light Remote Sensing Image on Recognition Accuracy of Phyllostachys edulis Stand Density

为探究无人机可见光影像在毛竹立竹度提取方面的适用性,以福建省三明市永安市天宝岩国家级自然保护区毛竹林为研究对象,运用重采样的方式获取不同空间分辨率(0.25、0.50、0.75 m)下的可见光影像,利用面向对象多尺度分割确定各个分辨率下的最佳分割尺度,采用随机森林分类方法,对不同空间分辨率下立竹度提取精度进行了比较。结果表明:不同空间分辨率下分割尺度的选择不同,影像空间分辨率为0.25、0.50、0.75 m的最佳分割尺度分别为20、9和8,立竹度提取平均精度分别为80.97%、81.29%和77.82%,Kappa系数分别为0.806 0、0.863 3和0.817 1;图像空间分辨率为0.50 m时,总体分类精度和Kappa系数最高,因此,毛竹立竹度提取的适宜图像空间分辨率为0.5 m,最佳分割尺度为9。

In order to explore the applicability of UAV visible image in the extraction of Phyllostachys edulis standing degree, the P. edulis forest in Tianbaoyan National Nature Reserve, Yongan City, Sanming City, Fujian Province, was taken as the research object, and the visible image under different spatial resolutions(0.25, 0.5 and 0.75 m) was obtained by resampling method. Object-oriented multi-scale segmentation was used to determine the optimal segmentation scale under each resolution, and random forest classification was used to compare the extraction accuracy of P. edulis stand density under different spatial resolutions. The results show that: the best segmentation scale for the segmentation scale to select different under different image spatial resolution of 0.25, 0.50 and 0.75 m are 20, 9 and 8, P. edulis stand density extract accuracy are 80.97%, 81.29% and 77.82%, and Kappa coefficients are 0.806 0, 0.863 3 and 0.817 1, respectively. When the spatial resolution of image is 0.50 m, the overall classification accuracy and Kappa coefficient are the highest. Therefore, the appropriate spatial resolution of image extraction of P. edulis stand density is 0.5 m, and the optimal segmentation scale is 9.