A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation
This work addresses the problem of improving unmixing accuracy and uncertainty estimation in hyperspectral imaging for remote sensing applications, representing an incremental advancement.
The paper tackles hyperspectral unmixing by extending the normal compositional model to incorporate spatial priors and estimate endmember uncertainty, resulting in a spatial compositional model (SCM) that provides more accurate endmembers and abundances compared to state-of-the-art algorithms.
The normal compositional model (NCM) has been extensively used in hyperspectral unmixing. However, most of the previous research has focused on estimation of endmembers and/or their variability. Also, little work has employed spatial information in NCM. In this paper, we show that NCM can be used for calculating the uncertainty of the estimated endmembers with spatial priors incorporated for better unmixing. This results in a spatial compositional model (SCM) which features (i) spatial priors that force neighboring abundances to be similar based on their pixel similarity and (ii) a posterior that is obtained from a likelihood model which does not assume pixel independence. The resulting algorithm turns out to be easy to implement and efficient to run. We compared SCM with current state-of-the-art algorithms on synthetic and real images. The results show that SCM can in the main provide more accurate endmembers and abundances. Moreover, the estimated uncertainty can serve as a prediction of endmember error under certain conditions.