Relative Altitude Estimation of Infrared Thermal UAV Images Using SIFT Features

Unmanned Aerial Vehicles (UAVs) have become indispensable in various applications, including surveillance, urban scene analysis, and agricultural monitoring. Accurate altitude estimation is critical for UAV operations, especially in environments where traditional sensors like GPS, pressure altimeters, and radar may fail. This paper explores the use of infrared and thermal imaging for relative altitude estimation of UAVs, highlighting their significant advantages over traditional RGB images. Infrared and thermal imaging offer superior performance in low-light and adverse weather conditions, providing clearer visibility and more reliable feature detection. By leveraging the Scale-Invariant Feature Transform (SIFT) features, this approach utilizes the inherent benefits of thermal images to estimate altitude changes based on the size variations of matched keypoints in consecutive images. Experimental results on two infrared thermal UAV datasets demonstrate the effectiveness of this approach, showing substantial improvements in estimation accuracy when combined with Siamese networks for enhanced feature matching.