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The accurate and reliable high-altitude orientation estimation is of great significance for unmanned aerial vehicles (UAVs) localization, and further assists them to conduct some fundamental functions, such as aerial mapping, environmental monitoring, and risk management. However, the traditional orientation estimation is susceptible to electromagnetic interference, high maneuverability, and substantial scale variations. Hence, this paper aims to present a new visual compass algorithm to estimate the orientation of a UAV employing the appearance and geometry structure of the point and line features in the remote sensing images. In this study, a coarse-to-fine feature tracking method is used to locate the matched keypoints precisely. An LK-ZNCC algorithm is proposed to match line segments in real-time. A hierarchical fusion method for point and line features is designed to expand the scope of the usage of this system. Many comparative experiments between this algorithm and others are conducted on a UAV. Experimental results show that the proposed visual compass algorithm is a reliable, precise, and versatile system applicable to other UAV navigation systems, especially when they do not work in particular situations. [ABSTRACT FROM AUTHOR]