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Low-altitude Unmanned Aerial Vehicles (UAV) images which include distortion, illumination variance, and large rotation angles are facing multiple challenges of image orientation and image processing. In this paper, a robust and convenient photogrammetric approach is proposed for processing low-altitude UAV images, involving a strip management method to automatically build a standardized regional aerial triangle (AT) network, a parallel inner orientation algorithm, a ground control points (GCPs) predicting method, and an improved Scale Invariant Feature Transform (SIFT) method to produce large number of evenly distributed reliable tie points for bundle adjustment (BA). A multi-view matching approach is improved to produce Digital Surface Models (DSM) and Digital Orthophoto Maps (DOM) for 3D visualization. Experimental results show that the proposed approach is robust and feasible for photogrammetric processing of low-altitude UAV images and 3D visualization of products.
Ai et al. (Thu,) studied this question.