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Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability.

, , and . CoRR, (2018)

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Cross-validating Gaussian process methods for hyperspectral data from tree crowns., and . IGARSS, page 3214-3217. IEEE, (2017)Comparison of linear and morphological shared-weight neural networks., and . Electronic Imaging: Nonlinear Image Processing, volume 2662 of SPIE Proceedings, page 0000. SPIE, (1996)A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation., , and . CoRR, (2015)Choquet integral-based morphological operators., and . Electronic Imaging: Nonlinear Image Processing, volume 3646 of SPIE Proceedings, SPIE, (1999)A Gaussian mixture model representation of endmember variability in hyperspectral unmixing., , and . CoRR, (2017)Multi-stream continuous hidden Markov models with application to landmine detection., , and . EURASIP J. Adv. Signal Process., (2013)Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra., , , , , , , , , and 2 other author(s). Remote. Sens., 11 (18): 2072 (2019)SPICEE: An Extension of SPICE for Sparse Endmember Estimation in Hyperspectral Imagery., , and . IEEE Geosci. Remote. Sens. Lett., 13 (12): 1910-1914 (2016)Random set framework for multiple instance learning., , , and . Inf. Sci., 181 (11): 2061-2070 (2011)Context Dependent Spectral Unmixing., , and . MLSP, page 1-6. IEEE, (2012)