Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images

Dobigeon, Nicolas; Févotte, Cédric

doi:10.1109/whispers.2013.8080681

Cited by 16 publications

(18 citation statements)

References 22 publications

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“…In the proposed model, all additive terms are gathered in the vector φ n . Note that a similar model, called robust LMM, has been recently introduced in [14].…”

Section: Problem Formulationmentioning

confidence: 99%

Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection

Altmann

Dobigeon

McLaughlin

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with synthetic and real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

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“…In the proposed model, all additive terms are gathered in the vector φ n . Note that a similar model, called robust LMM, has been recently introduced in [14].…”

Section: Problem Formulationmentioning

confidence: 99%

Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection

Altmann

Dobigeon

McLaughlin

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the proposed model, all additive terms are gathered in the vector φ n . Note that a similar model, called robust LMM, has been recently introduced in [13].…”

Section: Problem Formulationmentioning

confidence: 99%

Nonlinear spectral unmixing of hyperspectral images using residual component analysis

Altmann

McLaughlin

2014

2014 Sensor Signal Processing for Defence (SSPD)

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This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

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“…NMF is a popular tool used in many application areas, including face recognition [1], musical signal processing [2] [3] and hyperspectral imaging [4], amongst others.…”

Section: Introductionmentioning

confidence: 99%

Non-negative matrix factorisation incorporating greedy Hellinger sparse coding applied to polyphonic music transcription

O'Hanlon

Sandler

Plumbley

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Non-negative Matrix Factorisation (NMF) is a commonly used tool in many musical signal processing tasks, including Automatic Music Transcription (AMT). However unsupervised NMF is seen to be problematic in this context, and harmonically constrained variants of NMF have been proposed. While useful, the harmonic constraints may be constrictive in mixed signals. We have previously observed that recovery of overlapping signal elements using NMF is improved through introduction of a sparse coding step, and propose here the incorporation of a sparse coding step using the Hellinger distance into a NMF algorithm. Improved AMT results for unsupervised NMF are reported.

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Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images

Cited by 16 publications

References 22 publications

Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection

Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection

Nonlinear spectral unmixing of hyperspectral images using residual component analysis

Non-negative matrix factorisation incorporating greedy Hellinger sparse coding applied to polyphonic music transcription

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