Sparse matching via point and line feature fusion for robust aerial triangulation of photovoltaic power stations’ thermal infrared imagery

Abstract. In this paper, we present a novel matching method tailored for unmanned aerial vehicle (UAV) thermal infrared images of photovoltaic (PV) panels characterized by highly repetitive textures. This method capitalizes on the integration of point and line features within the image to obtain reliable corresponding points. Furthermore, it employs multiple constraints to eliminate mismatched features and get rid of the interference of repetitive textures on feature matching. To verify the effectiveness of the proposed method, we used an UAV equipped with the DJI Zenmuse H20T thermal infrared gimbal to capture 3767 images of a PV power station in Guangzhou, China. Experiments demonstrate that, for UAV thermal infrared images of PV panels, our method outperforms the state-of-the-art techniques in terms of the density of matching points, matching success rate and matching reliability, consequently leading to robust aerial triangulation results.