Information extraction from hyperspectral imagery is highly affected by difficulties in accounting for flux density
variation and Bidirectional reflectance effects. However, its full implementation requires extremely detailed information
regarding the spatial structures or mini-structure of each material. This information is frequently not available at the
accuracy needed (if it even exists). Thus, reflectance estimations for hyperspectral images will not fully account for flux
density effects and consequently, the reflectance of the same surface material would vary, resulting in increased spectral
confusion. Utilization of normalization, band selection, ratioing, spectral angle (SAM), and derivative techniques for this
purpose provide only partial solutions under unknown illumination conditions.
In this work we introduce a novel signal processing approach, based on wavelet analysis, aimed at reducing the effects of
flux density variations on imagery objects' identification. Wavelet analysis is a space localized periodic analysis tool,
which enables analysis of a signal in both spectral and frequency domains.
This new technique is based on the observation that detailed wavelet coefficients, which result from wavelet
decomposition, vary linearly with increasing scaling level. Since both the coefficient of variation of these linear
relationships (a) and reflectance (R) at each wavelength position are affected by flux density, their ratio (R2a) was
hypothesized to be invariant to flux density effects in particular and multiplicative effects in general.
Advantage of this method was supported by higher accuracies and reliabilities gained for classifying with R2a when
compared to classification of the real spectral images of Mediterranean and domestic plants and lithological formations.