全球定位系统2024，Vol.49Issue(3)：87-93,7.DOI:10.12265/j.gnss.2023221
基于稀疏和稠密图像匹配与对极约束的树高提取改进算法
Improved algorithm for tree height extraction based on sparse and dense image matching with epipolar constraints
摘要
Abstract
Tree height is a crucial parameter for monitoring forest conditions and photogrammetry stands out as an essential method for tree height acquisition due to its low cost and flexibility.As a passive remote sensing approach,the traditional photogrammetric method often requires a substantial quantity of images with high overlap,which is associated with the sparsity of traditional image features.To enhance tree height extraction accuracy under limited image availability,a proposed approach combines sparse feature matching with dense pixel matching,by employing the epipolar constraint to filter outliers,dense and highly accurate matching results are obtained.The three-dimensional reconstruction algorithm is then applied to generate a point cloud representing the forest scene.This method demonstrates the capability to reconstruct the forest scene comprehensively and extract tree heights even with a small number of images.Comparison with results from LiDAR point clouds yields a correlation coefficient of 0.91 and a maximum error of 1.64 meters.Notably,the algorithm requires only a small number of overlapping images,indicating its potential in handling high-resolution satellite imagery.关键词
无人机/图像匹配/对极约束/树高提取/单木分割Key words
unmanned aerial vehicle/image matching/epipolar constraints/tree height extraction/indi-vidual tree segmentation分类
天文与地球科学引用本文复制引用
蔡翔远,陈晓桐,李荣昊,魏江南,李帅,赵红颖..基于稀疏和稠密图像匹配与对极约束的树高提取改进算法[J].全球定位系统,2024,49(3):87-93,7.基金项目
国家自然科学基金(42130104) （42130104）
