Automatic sensor orientation using horizontal and vertical line feature constraints

Abstract

to assess the feasibility and effectiveness of the proposed method, simulated and real data are tested. The results demonstrate that, in cases with only 3 GCPs, the accuracy of the proposed method utilizing line features extracted automatically, is increased by 50%, compared to a BA using only point constraints.

A deep learning approach based on graphs to detect plantation lines

“…Linear objects also denominated linear features in the photogrammetric context, are common in images, especially in anthropic scenes. Consequently, they are used in several photogrammetric tasks, and examples of that may be found in past research, such as orientation or triangulation [ [1] , [2] , [3] , [4] ] rectification [ 5 , 6 ], matching [ 7 ], restitution [ 8 ] and camera calibration [ 9 , 10 ]. The registration of images and LiDAR (Light Detection And Ranging) data is also a topic that benefits from this type of linear object information [ 11 , 12 ].…”