Validation of spectral unmixing methods using photometry and topography information
In this work the performance of spectral unmixing procedures applied on hyperspectral images of granular mixtures are compared. For that purpose we consider a laboratory image and synthetic images, the latter being created using a original algorithmic process while borrowing some aspects of the real data. The nonlinear effects of light multiple scattering within the mixture can be partially compensated by transformation of the image using the Hapke model under different levels of injected information regarding topography and photometry of the scene. The validation of the different methods and deconvolution processes is established by comparing the results with a “ground truth” through the mean Spectral Angle for the extracted endmembers and the mean Abundances Root Mean Square Error for the estimated abundances. Interpretation of the results indicates that is safer to extract the endmembers from the original version of image unless topography and most importantly photometry are precisely known. On the other hand better distribution maps are obtained in general from the transformed version of the image.