GLUP: Yet another algorithm for blind unmixing of hyperspectral data

Ammanouil, Rita; Ferrari, A.; Richard, Cédric; Mary, David

doi:10.1109/whispers.2014.8077546

Cited by 6 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the performance of the proposed detector, we generated 8000 synthetic samples by mixing the three collected spectra. Among the 8000 pixels, 4000 were generated using the linear model in (2), and 4000 using the modified generalized bilinear model in (38). A fixed abundance vector α = [0.6, 0.4, 0.1] ⊤ was used for all samples.…”

Section: Simulations With Known Mmentioning

confidence: 99%

See 1 more Smart Citation

Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

Imbiriba

Bermudez

Richard

et al. 2016

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

Mixing phenomena in hyperspectral images depend on a variety of factors, such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing problems. The simplest one is the linear mixing model. Nevertheless, it has been recognized that the mixing phenomena can also be nonlinear. The corresponding nonlinear analysis techniques are necessarily more challenging and complex than those employed for linear unmixing. Within this context, it makes sense to detect the nonlinearly mixed pixels in an image prior to its analysis, and then employ the simplest possible unmixing technique to analyze each pixel. In this paper, we propose a technique for detecting nonlinearly mixed pixels. The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model. The two errors are combined into a detection statistics for which a probability density function can be reasonably approximated. We also propose an iterative endmember extraction algorithm to be employed in combination with the detection algorithm. The proposed detect-then-unmix strategy, which consists of extracting endmembers, detecting nonlinearly mixed pixels and unmixing, is tested with synthetic and real images.

show abstract

Section: Simulations With Known Mmentioning

confidence: 99%

“…The GBM (38) was used for the first image, while the PNMM (43) with ξ = 3 was considered for the second image. The SNR was 21dB in both cases, and the abundances were drawn uniformly in the simplex.…”

Section: Simulations With Known Mmentioning

confidence: 99%

Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

Imbiriba

Bermudez

Richard

et al. 2016

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The literature on computing S and B is extensive. A first group of methods are the continuous methods, based on applying iterative descent algorithms like ADMM [6] or fast gradient [7] to a relaxed version of (1). In most works, the product of S and B is estimated directly, using another variable X " SB T that has to be arXiv:1711.02883v1 [math.OC] 8 Nov 2017 row sparse, with M « DX.…”

Section: A Combinatorial Formulation Of Sparse Codingmentioning

confidence: 99%

Spectral Unmixing With Multiple Dictionaries

Cohen

Gillis

2018

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

Spectral unmixing aims at recovering the spectral signatures of materials, called endmembers, mixed in a hyperspectral or multispectral image, along with their abundances. A typical assumption is that the image contains one pure pixel per endmember, in which case spectral unmixing reduces to identifying these pixels. Many fully automated methods have been proposed in recent years, but little work has been done to allow users to select areas where pure pixels are present manually or using a segmentation algorithm. Additionally, in a non-blind approach, several spectral libraries may be available rather than a single one, with a fixed number (or an upper or lower bound) of endmembers to chose from each. In this paper, we propose a multiple-dictionary constrained low-rank matrix approximation model that address these two problems. We propose an algorithm to compute this model, dubbed M2PALS, and its performance is discussed on both synthetic and real hyperspectral images.Index Terms-spectral unmixing, hyperspectral imaging, nonnegative matrix factorization, endmember extraction algorithms.

show abstract

“…Indeed, the specified constraints can be handled independently from the rest of the problem and often lead to analytical solutions when solving the resulting optimization problem. Using this fruitful principle, an ADMM-based algorithm for linear unmixing using a group lasso 2,1 -norm regularization was recently developed in [15], [16]. Inspired by these examples, this paper proposes to exploit the advantages of an ADMM-based resolution of the arXiv:1502.01260v2 [stat.ME] 20 Oct 2015…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model

Thouvenin

Dobigeon

Tourneret

2016

IEEE Trans. Signal Process.

172

157

View full text Add to dashboard Cite

Abstract-Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference spectral signatures composing the data -referred to as endmembers -their abundance fractions and their number. In practice, the identified endmembers can vary spectrally within a given image and can thus be construed as variable instances of reference endmembers. Ignoring this variability induces estimation errors that are propagated into the unmixing procedure. To address this issue, endmember variability estimation consists of estimating the reference spectral signatures from which the estimated endmembers have been derived as well as their variability with respect to these references. This paper introduces a new linear mixing model that explicitly accounts for spatial and spectral endmember variabilities. The parameters of this model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unmixing method is evaluated on synthetic and real data. A comparison with state-of-the-art algorithms designed to model and estimate endmember variability allows the interest of the proposed unmixing solution to be appreciated.

show abstract

GLUP: Yet another algorithm for blind unmixing of hyperspectral data

Cited by 6 publications

References 15 publications

Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

Spectral Unmixing With Multiple Dictionaries

Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model

Contact Info

Product

Resources

About