Improved algorithm for tree height extraction based on sparse and dense image matching with epipolar constraints

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.