Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery

Dobigeon, Nicolas; Moussaoui, Saïd; Coulon, Martial; Tourneret, Jean-Yves; Hero, Alfred O.

doi:10.1109/tsp.2009.2025797

Cited by 333 publications

(296 citation statements)

References 54 publications

(150 reference statements)

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“…Statistical methods: In the statistical framework, spectral unmixing is formulated as a statistical inference problem by adopting a Bayesian methodology where the inference engine is the posterior density of the random objects to be estimated as described for example in [20,21,22].…”

Section: Related Workmentioning

confidence: 99%

Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images

Averbuch

Zheludev

2012

Remote Sensing

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The goal of the paper is to detect pixels that contain targets of known spectra. The target can be present in a sub-or above pixel. Pixels without targets are classified as background pixels. Each pixel is treated via the content of its neighborhood. A pixel whose spectrum is different from its neighborhood is classified as a "suspicious point". In each suspicious point there is a mix of target(s) and background. The main objective in a supervised detection (also called "target detection") is to search for a specific given spectral material (target) in hyperspectral imaging (HSI) where the spectral signature of the target is known a priori from laboratory measurements. In addition, the fractional abundance of the target is computed. To achieve this we present two linear unmixing algorithms that recognize targets with known (given) spectral signatures. The CLUN is based on automatic feature extraction from the target's spectrum. These features separate the target from the background. The ROTU algorithm is based on embedding the spectra space into a special space by random orthogonal transformation and on the statistical properties of the embedded result. Experimental results demonstrate that the targets' locations were extracted correctly and these algorithms are robust and efficient.

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Section: Related Workmentioning

confidence: 99%

Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images

Averbuch

Zheludev

2012

Remote Sensing

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“…Recently, this assumption has been relaxed by applying a single step hierarchical Bayesian approach, called Bayesian linear unmixing (BLU). The resulting algorithm required the number R of factors to be specified (see [10] for details). Here we extend BLU to a fully unsupervised algorithm that generates samples distributed according to the joint posterior distribution of R and the other model parameters, from which a Bayesian estimator of R can be derived.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The BLU algorithm studied in [10] generates an estimate of the posterior distribution of M and A given the number R of factors for appropriate prior distributions assigned to the mixing parameters in (1). Moreover, the residual errors in (1) are assumed to be independent identically distributed (i.i.d.)…”

Section: Unsupervised Bayesian Linear Unmixingmentioning

confidence: 99%

“…A multivariate Gaussian distribution (MGD) truncated on a subset T r is chosen as prior distribution for the projected factors t r . The subset T r is defined so that the non-negativity constraint on m r is ensured (see [10]). The mean vectors e r of this truncated MGD are provided by an EEA dedicated to hyperspectral imagery and the variances s 2 r are fixed to a large value.…”

Section: Unsupervised Bayesian Linear Unmixingmentioning

confidence: 99%

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Unsupervised Bayesian analysis of gene expression patterns

Bazot

Dobigeon

Tourneret

et al. 2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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In this paper we introduce a new method for analyzing expression patterns from high throughput and complex data such as gene expression microarrays. These microarrays are collected under different conditions such as time, phenotype and treatment. The proposed method uses a Bayesian matrix decomposition, called Bayesian linear unmixing (BLU), to extract a set of characteristic gene signatures, or factors, and a set of coefficients, factor scores, that specify the relative contribution of each signature to a specific sample. BLU is related to Bayesian factor analysis but differs in an important respect: BLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Thus BLU reduces the multiplexing of genes into different factors and can enhance interpretability of the factor loadings and factor scores. The unsupervised version of BLU presented in this paper also provides estimates of the number of factors. We illustrate the application of BLU to bioinformatics by analyzing gene expression microarray datasets.

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“…Dobigeon and Brun [7] compared the results obtained by applying PCA, independent component analysis (ICA) [8], vertex component analysis (VCA) [9], and Bayesian linear unmixing (BLU) [10] to experimental EELS-HSI data. They eventually found that BLU provided the most plausible spatial distributions for the constituent spectral components, presumably because of its more realistic modeling of the EELS-HSI data.…”

Section: Introductionmentioning

confidence: 99%

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Shiga

Muto

2018

Nanoinformatics

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Recent advances in scanning transmission electron microscopy (STEM) techniques have enabled us to obtain spectroscopic datasets such as those generated by electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectroscopy measurements in a PC-controlled way from a specified region of interest (ROI) even at atomic scale resolution, also known as hyperspectral imaging (HSI). Instead of conventional analytical procedures, in which the potential constituent chemical components are manually identified and the chemical state of each spectral component is successively determined, a statistical machine-learning approach, which is known to be more effective and efficient for the automatic resolution and extraction of the underlying chemical components stored in a huge three-dimensional array of an observed HSI dataset, is used. Among the statistical approaches suitable for processing HSI datasets, methods based on matrix factorization such as principal component analysis (PCA), multivariate curve resolution (MCR), and nonnegative matrix factorization (NMF) are useful to find an essential low-dimensional data subspace hidden in the HSI dataset. This chapter describes our developed NMF method, which has two additional terms in the objective function, and which is particularly effective for analyzing STEM-EELS/EDX HSI datasets: (i) a soft orthogonal penalty, which clearly resolves partially overlapped spectral components in their spatial distributions and (ii) an automatic relevance determination (ARD) prior, which optimizes the number of components involved in the observed

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Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery

Cited by 333 publications

References 54 publications

Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images

Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images

Unsupervised Bayesian analysis of gene expression patterns

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

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