When fielding near-surface hyperspectral imaging systems for computer vision applications, raw data from a sensor are often corrected to reflectance before analysis. This research presents an expedient and flexible methodology for performing spectral reflectance estimation using in situ asphalt cement concrete or Portland cement concrete pavement as a reference material. Then, to evaluate this reflectance estimation method’s utility for computer vision applications, four datasets are generated to train machine learning models for material classification: (1) a raw signal dataset, (2) a normalized dataset, (3) a reflectance dataset corrected with a standard reference material (polytetrafluoroethylene), and (4) a reflectance dataset corrected with a pavement reference material. Various machine learning algorithms are trained on each of the four datasets and all converge to excellent training accuracy (>94 % ). Models trained on the raw or normalized signals, however, did not exceed 70% accuracy when tested against new data captured under different illumination conditions, while models trained using either reflectance dataset saw almost no drop between training and testing accuracy. These results quantify the importance of reflectance correction in machine learning workflows using hyperspectral data, while also confirming practical viability of the proposed reflectance correction method for computer vision applications.