a multi-view dense matching algorithm of high resolution aerial images based on graph network

A multi-view dense matching algorithm of high resolution aerial images based on graph network was presented. Overlap ratio and direction between adjacent images was used to find the candidate stereo pairs and build the graph network, then a Coarse-to-Fine strategy based on modified semi-global matching algorithm (SGM) was adapted to calculate the disparity map of stereo pairs. Finally, dense point cloud was generated and fused using a multi-triangulation method based on graph network. In the experiment part, the Vaihingen aerial image dataset and the oblique nadir image block of Zürich in ISPRS/EuroSDR project were used to test the algorithm above. Experiment results show that out method is effective for multi-view dense matching of high resolution aerial images in consideration of completeness, efficiency and precision, while the RMS of average reprojection error can reach subpixel level and the actual deviation is better than 1.5 GSD. Due to the introduction of guided median filter, regions of sharp discontinuities, weak textureness or repeat textureness like buildings, vegetation and water body can also be matched well.