Large-Rotation-Angle Photogrammetric Resection Based on Least-Squares Homotopy Iteration Method

Abstract

Inclined imaging is an advanced sensing technique, and has been extensively used. Therefore, large-rotation angle photogrammetric resection has become an important topic. However, traditional iterative methods are limited by requiring good initial values. By contrast, noniterative methods do not require an initial value, although they exhibit relatively low accuracy and robustness. To obtain results with superior precision and universality, this letter proposes an improved approach by modifying the initial value acquisition and iterative methods. This algorithm uses nonlinear iteration to reduce the model error, thereby possibly achieving an exceptional convergence for large-rotation-angle photogrammetric resection. Experimental results on the real data indicate that the proposed algorithm outperforms the previous methods.