Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmospherically
compensated imagery. Rather than compensate the imagery, a more recent approach uses physical
models to generate radiance signature spaces. The signature space is actually a representation of what the target
might look like to the sensor as the reflectance propagates through the atmosphere. The model takes into account
atmospherics, illumination conditions and target reflectivity. It is well known that the directional characteristics
of reflectance vary considerably fromspecular to ideally diffuse (i.e., Lambertian). The current physical models
assume the world is Lambertian. However, the reflectance properties are a function of wavelength, illumination
angle, and viewing angle. The bidirectional reflectance distribution function (BRDF), which is actually a scattering
function analogous to the angular scattering coefficient, describes the bidirectional reflectance values of all
combinations of input-output angles and wavelength. This paper examines the impact of using the Lambertian
assumption as it relates to physics bases material detection.
The bidirectional reflectance studied is parameterized, based on laboratory measurements, using the Beard-
Maxwell model. This parameterized reflectance is then coupled to the physics-based sensor-reaching radiance
model to generate signature spaces. The signature spaces, along with hyperspectral imagery, are used in a target
detection scheme where results are assessed through visual analysis.