Graph-regularized coupled spectral unmixing for multisensor time-series analysis

Abstract

A new methodology that solves unmixing problems involving a set of multisensor time-series spectral images is proposed in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems via regularization on graphs between the multisensor time-series data to obtain robust and stable unmixing solutions beyond data modalities owing to different sensor characteristics and the effects of non-optimal atmospheric correction. A synthetic dataset that includes seasonal and trend changes on the surface and the residuals of non-optimal atmospheric correction is used for numerical validation. Experimental results demonstrate the effectiveness of the proposed methodology.