Multi-temporal hyperspectral data classification without explicit reflectance correction

In order to be independent from light source and atmospheric conditions, radiance values extracted from a remote hyperspectral image have to be converted into reflectance values before data processing. Several methods have been proposed in the literature but they require that the lighting/and or atmospheric conditions to be estimated. In the framework of supervised classification, we propose an approach to deal with such lighting and atmospheric temporal fluctuations without reference measurement. Assuming that materials to the surface objects to be discriminated is Lambertian, we show that the difference in lighting conditions after a log-transformation of both reflectance and radiance signals can be expressed as an additive effect. This effect remains additive after the use of a linear dimension reduction method and can be efficiently estimated in a low dimensional feature space. In the feature space, this difference in ligthing can be estimated and thus corrected by finding the translation for which the class densities obtained for each image best overlaps (using cross correlation). This novel approach was applied on a remote sensing data set over the Quiberon peninsula France. For the tested images, classification results obtained with this approach were comparable to those obtained using a classical reflectance correction technique.