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Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing

2022-02-07 05:26:32
Hongming Li, Shujian Yu, Jose C. Principe
arXiv_CV
     
Abstract

We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{é}nyi's $\alpha$-order entropy functional, our network can be directly optimized by stochastic gradient descent (SGD), without any variational approximation or adversarial training. As a solid application, we evaluate our ICA in the problem of hyperspectral unmixing (HU) and refute a statement that "\emph{ICA does not play a role in unmixing hyperspectral data}", which was initially suggested by~\cite{nascimento2005does}. Code and additional remarks of our DDICA is available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2202.02951

PDF

https://arxiv.org/pdf/2202.02951.pdf

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