基于主动轮廓演变模型的遥感影像单棵树木检测

Remote Sensing Image Single Tree Detection Based on Active Contour Evolution Model

单木检测是一种将遥感影像和计算机视觉技术相结合自动或半自动获取单棵树木信息的方法。针对复杂森林场景中大量树木相互覆盖的现象,以及树冠内部大量弱边缘导致的树冠顶点过度提取和树冠轮廓描绘不精细的问题,提出了一种基于主动轮廓演变模型的遥感影像单棵树木检测方法。该方法基于树荫和树木数量正相关的先验知识划分阴影控制区域并将区域形心作为树冠顶点;接着使用光照角度优化的形态主动轮廓演变模型(Snake模型)进行树冠轮廓描绘,使其可以越过弱边界点;最后依照形状特征优化树冠轮廓。实验结果表明,该方法提高了复杂森林场景下的单棵树木信息提取的准确度,降低了树冠顶点提取过程的误识率,使树冠轮廓形状更加准确。

Single-wood detection is a method of automatically or semi-automatically acquiring single tree information by combining remote sensing imagery with computer vision technology.Aiming at the phenomenon that a large number of trees cover each other in complex forest scenes,and the excessive extraction of crown vertices and the outline of crown caused by a large number of weak edges inside the crown,a remote tree image detection method based on active contour evolution model is proposed.The method divides the shadow control area based on the prior knowledge of the positive correlation between the number of shades and the number of trees,and uses the shape centroid as the crown apex.Then the morphological active contour evolution model(Snake model)optimized by the illumination angle is used to describe the crowncontour,so that it can cross the weak boundary point;finally optimize the crown profile according to the shape feature.The experimental results show that the method improves the accuracy of single tree wood information extraction in complex forest scenes,reduces the misrecognition rate of crown extraction process,and makes the crown contour shape more accurate.