The ambiguity about the actual illumination of a surface, causing uncertainty about its spectral reflectance, has been recognized as the color constancy problem. In multispectral remote sensing, this leads to erroneous classification and segmentation as well as spurious results of change detection. We introduce a transformation which is invariant against surface orientation. The suggested invariant is a linear mapping in the logarithmic feature space and filters out all spectral information which can possibly stem from an illumination change rather than from the reflectance of a given surface. Instead of recovering the reflectance signal, the suggested mapping produces a new only surface reflectance-dependent descriptor which is invariant against varying illumination.
Sole input is the relative direct to diffuse illumination spectrum.
No assumptions about the possible reflectance spectra are made. Error propagation through the transform is well understood. Apart from simulated and in situ measured data, the suggested transform has been successfully applied to experimental multispectral imagery. The quantitative results and example clippings from the imagery show significant improvements in the multispectral classification of target surfaces under varying surface orientation. Although the transformed data may not completely supersede the original spectral data, the suggested transformation is shown to be a powerful early processing step, allowing subsequent orientation invariant classification, edge detection and segmentation.