A novel approach to generating DSM from high-resolution UAV images

In the past few years, unmanned aerial vehicles (UAVs) demonstrated their great potential for photogrammetric measurements in a lot of application fields because its less expensive, safer and higher resolution images. Nevertheless, their images are often affected by large rotation, big view-point change and small overlaps. In this paper, we present a novel approach for reliable Digital Surface Models (DSM) generation, which is designed to operate on high-resolution, wide-baseline UAV image sets and compute dense 3D point clouds efficiently. It is implemented as a procedure including the four steps of match, expand, filter and reconstruction, starting from a sparse set of matched difference-of-Gaussian (DoG) keypoints, forming a triangulation on it, then expanding per-pixel under local parallax continuity, using visibility constraints to filter false matches, finally generating the DSM. Experiments are conducted to demonstrate the effectiveness and accuracy of our approach and to show that state-of-the-art performance can be achieved with significant acceleration.