Abstract:
Environmental, illumination, atmospheric, and temporal changes cause hyperspectral image spectral variability. It may cause significant unmixing estimation errors. Extended linear mixing models generate large-scale nonsmooth ill-posed inverse problems to address this issue.
Regularization strategies used to obtain meaningful results have introduced interdependencies among abundance solutions, which further complicates the optimization problem. This paper introduces a data-dependent multiscale hyperspectral unmixing model that accounts for spectral variability.
A superpixel-based multiscale transform adds spatial contextual information to extended linear mixing model abundances.
The proposed method yields a fast algorithm that solves the abundance estimation problem once per scale per iteration. Simulations using synthetic and real images compare the proposed algorithm and other state-of-the-art solutions in accuracy and execution time.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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