期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 25, 期 7, 页码 3219-3232出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2016.2562562
关键词
Hyperspectral imaging; remote sensing; source separation; tensor decomposition
资金
- European Research Council [2012-ERC-AdG-320684]
- Direct For Mathematical & Physical Scien [1118971] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [1118971] Funding Source: National Science Foundation
In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant. The spectral signatures and fractional abundances of the pure materials in the scene are seen as latent variables, and assumed to follow a general dynamical structure. Based on a simplified version of this model, we derive an efficient spectral unmixing algorithm to estimate the latent variables by performing alternating minimizations. The performance of the proposed approach is demonstrated on synthetic and real multitemporal hyperspectral images.
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