Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images

Simon Henrot 1, 2 Jocelyn Chanussot 1 Christian Jutten 2
1 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
2 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
Abstract : 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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Simon Henrot, Jocelyn Chanussot, Christian Jutten. Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2016, 25 (7), pp.3219 - 3232. ⟨10.1109/TIP.2016.2562562⟩. ⟨hal-01346918⟩

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