Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability
arxiv(2024)
摘要
Given a hyperspectral image, the problem of hyperspectral unmixing (HU) is to
identify the endmembers (or materials) and the abundance (or endmembers'
contributions on pixels) that underlie the image. HU can be seen as a matrix
factorization problem with a simplex structure in the abundance matrix factor.
In practice, hyperspectral images may exhibit endmember variability (EV)
effects – the endmember matrix factor varies from one pixel to another. In
this paper we consider a multilayer simplex-structured matrix factorization
model to account for the EV effects. Our multilayer model is based on the
postulate that if we arrange the varied endmembers as an expanded endmember
matrix, that matrix exhibits a low-rank structure. A variational
inference-based maximum-likelihood estimation method is employed to tackle the
multilayer factorization problem. Simulation results are provided to
demonstrate the performance of our multilayer factorization method.
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