基于分水岭线的CV模型树冠分割优化算法

Crown segmentation algorithm model based on watershed and morphological Chan-Vese

为了实现对森林调查中的树木生长状况精确监测,单体树冠分割是重要研究内容。将无人机拍摄的云杉林影像作为研究对象,针对图像中存在的树冠粘连问题,结合分水岭算法和Chan-Vese主动轮廓模型,建立一套自动实现树冠轮廓定位、分割、优化系统。该改进算法将预处理后图像输入到标记分水岭分割中,得到的分水线作为Chan-Vese模型的基础,通过曲线演化至图像特定边缘,最大程度提取出完整的树冠轮廓。为改善分水岭算法敏感、区域主动轮廓模型定位初始位置难确定等问题,提出一种便捷完整的树冠分割的算法。实验结果表明,与传统分水岭和传统Chan-Vese模型相比,树冠提取方法准确率达到82.62%,实现较高提升。并且在对树冠误判、漏判、欠分割、过分割问题有较好改进。该方法可以自动、有效地提取出独立的树冠轮廓,并具有更好的提取精度。

In order to accurately monitor the growth status of trees in forest surveys,single tree crown segmentation is an important research topic.Taking the spruce forest image taken by UAV as the research object,aiming at the problem of crown adhesion in the image,combining watershed algorithm and Chan-Vese active contour model,a set of automatic crown contour positioning,segmentation and optimization system was established.This improved algorithm inputs the preprocessed image into the labeled watershed segmentation,and the resulting watershed serves as the basis for the CV model.The curve is evolved to a specific edge of the image,maximizing the extraction of the complete crown contour.The research method in this paper solves the problem of serious over segmentation and under segmentation of watershed algorithm,and the problem of difficult to determine the initial location of regional active contour model,thus obtaining a convenient and complete algorithm for tree crown segmentation.The experimental results show that compared with traditional watershed and Chan-Vase models,the accuracy of the crown extraction method studied in this paper reaches 82.62%,achieving a high improvement.And there are good improvements in addressing issues such as crown misjudgment,missed judgment,under segmentation,and over segmentation.This method can automatically and effectively extract independent tree crown contours with better extraction accuracy.