The detection and recognition of foreign object debris (FOD) on low-altitude takeoff and landing runways are of critical importance to aircraft performance and airport operational safety. A FOD dataset was created and a physics-based FOD detection and identification approach was suggested in order to address the issues caused by the small size, lack of distinctive visual traits, and restricted availability of public data for FOD. Initially, multispectral images of common FOD materials were collected to establish a multispectral FOD dataset. Based on this dataset, attribute analysis was conducted on common FOD types, including metal rust and strong reflection phenomena. Utilizing the reflectivity of asphalt backgrounds, a pseudo-reflectivity metric relative to asphalt was derived for different materials. Through various statistical measures, the separability of different materials was preliminarily validated. Subsequently, a neural network with an encoder-decoder architecture featuring densely nested connections was proposed, using pseudo-reflectivity as input for material identification. The suggested approach outperformed existing material identification and segmentation models with similar parameter ranges on the test set of this dataset, achieving an accuracy of 0.9993 and a Macro-F1 score of 0.7749.