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Selection of Optimal Building Facade Texture Images From UAV-Based Multiple Oblique Image Flows
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2021-02-01 , DOI: 10.1109/tgrs.2020.3023135 Guoqing Zhou , Xin Bao , Siqi Ye , Haoyu Wang , Hongbo Yan
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2021-02-01 , DOI: 10.1109/tgrs.2020.3023135 Guoqing Zhou , Xin Bao , Siqi Ye , Haoyu Wang , Hongbo Yan
Oblique photogrammetry with multiple cameras onboard unmanned aerial vehicle (UAV) has been widely applied in the construction of photorealistic three-dimensional (3-D) urban models, but how to obtain the optimal building facade texture images (BFTIs) from the abundant oblique images has been a challenging problem. This article presents an optimization method for selection of BFTIs from the image flows acquired by five oblique cameras onboard UAV. The proposed method uses multiobjective functions, which consists of the smallest occlusion of the BFTI and the largest façade texture area, to select the optimal BFTIs. Geometric correction, color equalization, and texture repairment are also considered for correction of BFTI’s distortions, uneven color, and occlusion by other objects such as trees. Visual C++ and OpenGL under the Windows Operating System are used to implement the proposed methods and algorithms. The proposed method is verified using 49 800 oblique images collected by five cameras onboard the Matrice 600 Pro (M600 Pro) UAV system over Dongguan Street, in the City of Ji’nan, Shandong, China. To restore the partially occluded textures, different thresholds and different sizes of windows are experimented, and a template window of $200\times200$ pixels2 is recommended. With the proposed method, 2740 BFTIs are extracted from 49 800 oblique images. As compared with the Pix4Dmapper and Smart 3-D method, it can be concluded that the optimal texture can be selected from the image flow acquired by multiple cameras onboard UAV and the approximately 95% memory occupied by the original BFTIs is reduced.
更新日期：2021-02-01
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