Airborne Hyperspectral Image Georeferencing Aided by High-resolution Satellite Images

Over the past decade, airborne hyperspectral systems have shown remarkable performance in identifying and classifying a variety of ground objects, such as differentiating between minerals, vegetations, artificial materials, water, etc. Though the hyperspectral imaging market is still relatively small, yet it is steadily growing. Currently, most of the high performance systems are of the pushbroom camera type, and consequently, the sensor orientation of these systems heavily relies on the integrated GPS/INS (Global Positioning System/Inertial Navigation System) based direct georeferencing solution. In this study, an indirect georeferencing method is proposed that is based on utilizing robust image matching to high-resolution satellite imagery. This solution can be used in circumstances where GPS/INS-based georeferencing is not available or not feasible due to GPS signal loss and/or the lack of GPS infrastructure. The proposed method is motivated by the attractive properties of state-of-the-art high-resolution satellite imagery, including large swath width, high spatial and temporal resolution, and high positional accuracy. For robust image matching, a combination of SURF (Speeded-Up Robust Features) and RANSAC (RANdom SAmple Consensus) is utilized, and the trajectory modeling of the airborne hyperspectral pushbroom camera is based on the collinearity equation camera model with the GaussMarkov stochastic error model. Tests on simulation data showed encouraging performance results for the proposed approach. * Corresponding author.