Detection of spectrally varying BRDF materials in hyperspectral reflectance imagery

This study examines the influence of non-Lambertian reflectance effects on the detection of subpixel vehicles in hyperspectral imagery. Object-level BRDF spectral signatures for an olive green sedan were simulated using a fast, radiometrically accurate 3D rendering model, and the signatures were embedded as subpixel targets of low fractional fill into the HyMap hyperspectral image provided in the Rochester Institute of Technology Blind Test dataset. Detection algorithms based on the ACE detector were run on the scene. The results demonstrate a significant improvement in detection performance when including the target's BRDF variation in the detection scheme through either a subspace ACE or a Bayesian selection (multiple ACE detector) method.