Single tree crown extraction based on gray gradient image segmentation
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摘要: 树冠是树木的重要组成部分,基于遥感影像的树冠提取对于森林资源调查监测具有重要意义,但准确获得树冠的形状和边界比较困难。高分辨率影像具有丰富的纹理和光谱信息,基于高分辨率影像单木树冠勾勒技术为森林资源调查提供了一种快速有效的测树途径。但是,由于高分影像信息冗杂,面向对象的分割方法数据计算量大,并且需要人工设置光谱或纹理阈值才可以实现单木分割,导致工作效率下降,鲁棒性差。图像增强通过改变原始图像的结构关系,有选择地突出或者抑制图像中的某些特征,有效的图像增强有益于提高单木树冠分割的准确程度。因此,本文提出一种基于影像的灰度梯度图像分割的树冠提取方法,通过对比传统的罗伯斯、拉普拉斯算子与改进的数学形态学算子,利用目视解译与灰度直方图结合的方法确定最优选择为改进的数学形态学算子。然后,利用改进的数学形态学算子结合面向对象多尺度分割方法,简化原始影像复杂的背景信息,快速提取大范围单木树冠信息。以甘肃省张掖市大野口林区机载激光雷达系统携带的CCD影像为数据源,提取实验区单木树冠,并从空间和形状上验证效果。实验结果表明:在高分影像的灰度梯度图像上进行面向对象分割提取单木冠幅,单木株数精度为83.19%,形状精度达到88.62%,优于传统林业调查精度,且冠幅获取速度快,效率高,并可以较为精确地提取树冠边界。Abstract: Tree crown is an important part of trees. It is of significance to extract tree crown information based on remotely sensed images for forest resource inventory and monitoring. However, it’s difficult to extract the individual tree crown shape accurately. High spatial resolution image has an abundance of texture and spectral information, which provides a potentially efficient approach to delineate individual tree crown for forest resource inventory. However, with its abundance of information, the object-oriented image segmentation based on the original high resolution image has lower efficiency because of the large calculation and poor robustness since it needs setting spectrum or texture threshold manually. The method of image enhancement highlights or suppresses certain image features selectively by changing the image structure, so effective image enhancement can improve the accuracy and efficiency of the individual tree crown segmentation. In this article, a new gray-gradient image segmentation method was proposed to realize rapid and high accurate extraction of the individual tree crown. For the comparative analyses, we selected conventional Roberts and Laplacian operator, along with the proposed modified mathematical morphology operator as alternatives, subsequently, it was confirmed that the optimal operator was the modified mathematical morphology operator by combining visual interpretation and gradation histogram analysis. Furthermore, the modified mathematical morphology operator combined with object-oriented multiscale segmentation classification method was used for simplifying background information of raw image and extracting large-scaled single-tree crown information rapidly. To validate the efficiency of the method, CCD image of airborne laser radar in Dayekou forest region in Zhangye, Gansu Province of northwestern China was used to extract individual tree crown. The results showed that by using high spatial resolution gray gradient images, the location accuracy of tree crown was 83.19%, and the shape accuracy of crown was 88.62%, both of which were superior to the individual tree crown segmentation based on the original high spatial resolution image. The crown edges are drawn fast, efficiently and relatively accurate.
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