期刊
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
卷 6, 期 -, 页码 374-384出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCI.2019.2948726
关键词
Hyperspectral data; endmember variability; generative models; deep neural networks; variational autoencoders (VAE); spectral unmixing
资金
- National Council for Scientific and Technological Development (CNPq) [304250/2017-1, 409044/2018-0, 141271/2017-5, 204991/2018-8]
- Brazilian Education Ministry (CAPES) [PNPD/1811213]
Endmember (EM) spectral variability can greatly impact the performance of standard hyperspectral image analysis algorithms. Extended parametric models have been successfully applied to account for the EM spectral variability. However, these models still lack the compromise between flexibility and low-dimensional representation that is necessary to properly explore the fact that spectral variability is often confined to a low-dimensional manifold in real scenes. In this article we propose to learn a spectral variability model directly from the observed data, instead of imposing it a priori. This is achieved through a deep generative EM model, which is estimated using a variational autoencoder (VAE). The encoder and decoder that compose the generative model are trained using pure pixel information extracted directly from the observed image, what allows for an unsupervised formulation. The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model. Simulations using both synthetic and real data indicate that the proposed strategy can outperform the competing state-of-the-art algorithms.
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