Blind spatial unmixing of multispectral images: New methods combining sparse component analysis, clustering and non-negativity constraints

Karoui, Moussa Sofiane; Deville, Yannick; Hosseini, Shahram; Ouamri, Abdelaziz

doi:10.1016/j.patcog.2012.05.008

Cited by 40 publications

(22 citation statements)

References 26 publications

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“…The matrix corresponds to the set of estimated multispectral endmember-spectra that can be automatically derived by using multispectral unmixing processes with pure pixel assumption [8]- [10]. This matrix does not evolve during the updating stage of the proposed algorithm.…”

Section: Proposed Algorithmmentioning

confidence: 99%

A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

Benkouider

Karoui

ville

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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In these investigations, a novel algorithm is proposed for linearly unmixing hyperspectral images combined with multispectral data. This algorithm, which is used to unmix the considered hyperspectral image, is founded on nonnegative matrix factorization. It minimizes, with new multiplicative update rules, a novel cost function, which includes multispectral data and a spectral degradation model between these data and hyperspectral ones. The considered multispectral variables are also used to initialize the proposed algorithm. Tests, using synthetic data, are carried out to assess the performance of our algorithm and its robustness to spectral variability between the processed data. The obtained results are compared to those of state of the art methods. These tests prove that the proposed algorithm outperforms all other used approaches.Index Terms-Hyper/multispectral imaging, linear unmixing, multiplicative update rule, nonnegative matrix factorization, spectral degradation model

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Section: Proposed Algorithmmentioning

confidence: 99%

A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

Benkouider

Karoui

ville

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

Self Cite

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“…∈ ), containing in each of its row vectors a spectral band of an observable low spatial resolution multispectral (resp. high spatial resolution panchromatic) image (naturally, the multispectral and panchromatic images must be geometrically coregistered and radiometrically corrected), are modeled as [5] (K m (resp. K p ) denotes the number of pixels of the multispectral (resp.…”

Section: Data Modelmentioning

confidence: 99%

“…In this paper, we extend our techniques by proposing a new method for pan-sharpening multispectral remote sensing images. This new fusion method, which uses our recent developed Sparse Component Analysis (SCA)-based LSU approaches [4], [5], is based on Nonnegative Matrix Factorization (NMF) [6]. NMF methods consist in decomposing a nonnegative matrix into a product of two nonnegative matrices.…”

Section: Introductionmentioning

confidence: 99%

Linear spectral unmixing-based method including extended nonnegative matrix factorization for pan-sharpening multispectral remote sensing images

Karoui

2013

SPIE Proceedings

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This paper presents a new fusion approach for pan-sharpening multispectral remote sensing images. This approach, related to Linear Spectral Unmixing (LSU) techniques, includes Extended Nonnegative Matrix Factorization (ExNMF) for combining low spatial resolution multispectral and high spatial resolution panchromatic data. ExNMF is applied to different real multispectral and panchromatic data sets with different spatial resolutions and different number of spectral bands. The quality of pan-sharpened multispectral images is evaluated by the jointly spectral and spatial Quality with No Reference (QNR) index. Obtained results show that our proposed method outperforms the Principal Component Analysis (PCA) and Gram-Schmidt (GS)-based standard literature methods.

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“…1 It is properly pointed out in [18] that the term "deconvolution" is essentially wrong, since it actually denotes inversion of a convolution, a particular kind of integral transform that describes input-output relations of linear systems with memory [23]. As opposed to that, extraction of analytes from mixtures of overlapped spectra is related However, majority of SCA algorithms require that each analyte is active at certain spectral region alone [34,35,41,42]. This assumption is increasingly hard to satisfy when complexity of mixture grows and when, due to reasons elaborated previously, multiple analytes get overlapped.…”

Section: Introductionmentioning

confidence: 99%

Blind separation of analytes in nuclear magnetic resonance spectroscopy: Improved model for nonnegative matrix factorization

Kopriva

Jerić

2014

Chemometrics and Intelligent Laboratory Systems

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We introduce improved model for sparseness constrained nonnegative matrix factorization (sNMF) of amplitude mixtures nuclear magnetic resonance (NMR) spectra into greater number of component spectra. In proposed method selected sNMF algorithm is applied to the square of the amplitude of the mixtures NMR spectra instead to the amplitude spectra itself. Afterwards, the square roots of separated squares of components spectra and concentration matrix yield estimates of the true components amplitude spectra and of concentration matrix. Proposed model 2 remains linear in average when number of overlapping components is increasing, while model based on amplitude spectra of the mixtures moves away from the linear one when number of overlapping components is increased. That is demonstrated through conducted sensitivity analysis. Thus, proposed model improves capability of the sparse NMF algorithms to separate correlated (overlapping) components spectra from smaller number of mixtures NMR spectra.That is demonstrated on two experimental scenarios: extraction of three correlated components spectra from two 1 H NMR mixtures spectra and extraction of four correlated components spectra from three COSY NMR mixtures spectra. Proposed method can increase efficiency in spectral library search by reducing occurrence of false positives and false negatives. That, in turn, can yield better accuracy in biomarker identification studies which makes proposed method important for natural products research and the field of metabolic studies.

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Blind spatial unmixing of multispectral images: New methods combining sparse component analysis, clustering and non-negativity constraints

Cited by 40 publications

References 26 publications

A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

Linear spectral unmixing-based method including extended nonnegative matrix factorization for pan-sharpening multispectral remote sensing images

Blind separation of analytes in nuclear magnetic resonance spectroscopy: Improved model for nonnegative matrix factorization

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