Assessment of Asphalt Pavement Skid Resistance Using Ground-Based and UAV-Based Hyperspectral Synergy

Accurate assessment of the skid resistance of asphalt pavement is crucial for traffic safety. However, traditional detection methods suffer from inefficiency, high costs, and limited coverage, making them inadequate for large-scale road network monitoring. This paper proposes a method for assessing the skid resistance of asphalt pavements based on hyperspectral remote sensing. First, hyperspectral data of asphalt pavements with different aging degrees were acquired through ground-based spectral measurements, and feature bands correlated with the aging process were selected using the successive projections algorithm. Based on these results, the feature bands were applied to unmanned aerial vehicle (UAV)-based hyperspectral images to construct an aging spectral index capable of characterizing pavement aging conditions. Combined with the decision tree method, assessment of pavement aging conditions was achieved, with an overall accuracy of 96.52% and a Kappa coefficient of 0.948. Finally, a quantitative relationship model between the aging spectral index and skid resistance was established using regression analysis, with the coefficient of determination (R2) and root mean square error (RMSE) of the model being 0.869 and 3.26, respectively. The proposed method enables efficient, contactless and large-scale assessment of pavement skid resistance, expanding the application of UAV remote sensing technology in road maintenance.