Exploiting noisy hyperspectral bands for water analysis

This paper proposes a novel algorithm for the recovery of noisy bands from hyperspectral images. The method, based on spectral unmixing, relies on the spectral behavior of the materials on ground composing each image element. Firstly, reference spectra related to the classes of interest are used to perform spectral unmixing: these exhibit negligible noise influences as they are averaged over areas for which ground truth is available. After the unmixing process, the residual vector is mostly composed by the contributions of uninteresting materials, unwanted atmospheric influences and sensor-induced noise, and is thus ignored in the reconstruction of each spectrum. Finally, the value of a pixel in a given band is predicted as a combination of the noise-free endmembers, resulting in a signal with high signal-to-noise ratio in any spectral band. Experiments show that this method could be used to retrieve spectral information from corrupted bands, such as the ones placed at the edge between Ultraviolet and visible light frequencies, which are usually dominated by atmospheric effects and are thus discarded in practical applications. The proposed algorithm could then be exploited in the study of Coloured dissolved organic matter (CDOM) in natural waters.