Sparse hyperspectral unmixing is a widely used technique in remote sensing data characterization. It aims at inferring, from a large spectral library, the pure spectral signatures (endmembers) present in each pixel of a hyperspectral image, jointly with their corresponding abundances. The input to sparse unmixing is represented, thus, by a hyperspectral image acquired from a platform flying at high altitude and a spectral library compiled using laboratory measurements. The reflectance datacube results from a complex ensemble of algorithms which translate the digital numbers stored by the sensor to meaningful ground reflectance, including the removal of atmospheric influence. A recurrent question in the research community does not have an answer yet: how does the atmospheric composition at the time of the flight influence the fractional abundances retrieved via sparse unmixing? This is a fundamental question, as the atmospheric parameters are subject to uncertainties, being very difficult to know them in all pixels. In this paper, we investigate how the uncertainty in two atmospheric parameters: water vapor content and visibility range, propagates to the final abundance maps via atmospheric correction of the sensed image. Our experiments reveal that sparse unmixing is more robust to uncertainty in those parameters and performs better in terms of accuracy than unmixing with image-based endmembers.
['Marian-Daniel Iordache', 'Nitin Bhatia', 'José M. Bioucas-Dias', 'Antonio Plaza']
Uncertainty propagation from atmospheric parameters to sparse hyperspectral unmixing