Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model

Thouvenin, Pierre-Antoine; Dobigeon, Nicolas; Tourneret, Jean-Yves

doi:10.1109/tsp.2015.2486746

Cited by 172 publications

(177 citation statements)

References 38 publications

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“…Li et al [12] proposed a robust collaborative sparse regression method to spectrally unmix hyperspectral data based on a robust linear mixture model. Thouvenin et al [13] proposed a linear mixing model which explicitly accounts for spatial and spectral endmembers variability. Foody and Cox [14] used a linear mixture model and regression based fuzzy membership function to estimate land cover composition while in [15] the use of the VCA algorithm is demonstrated to unmix hyperspectral data with relatively lower computational complexity compared to other conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

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Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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“…In this work, we assume that an endmember estimated using a single image can be reasonably interpreted as a smooth deviation of a reference endmember described by a PLMM [10], thus allowing information redundancies to be exploited across time. Reminiscent of [22], the PLMM represents the measurement vector yn,t in the nth pixel of the tth image by a linear combination of the R endmembers corrupted by an additive term representing temporal variability.…”

Section: Problem Statementmentioning

confidence: 99%

“…To this end, 9 out of the 20 images have been corrupted by spatially sparse outliers. Moreover, each HS image has been generated as the linear mixture of 3 endmembers affected by smooth time-varying variability -generated as in [10] -whose abundances vary smoothly over time except for the corrupted pixels. An additive white Gaussian noise (ensuring a signal-to-noise ratio between 25 and 30 dB) has been finally added to the mixtures.…”

Section: Experiments With Synthetic Datamentioning

confidence: 99%

Unmixing multitemporal hyperspectral images accounting for smooth and abrupt variations

Thouvenin

Dobigeon

Tourneret

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-A classical problem in hyperspectral imaging, referred to as hyperspectral unmixing, consists in estimating spectra associated with each material present in an image and their proportions in each pixel. In practice, illumination variations (e.g., due to declivity or complex interactions with the observed materials) and the possible presence of outliers can result in significant changes in both the shape and the amplitude of the measurements, thus modifying the extracted signatures. In this context, sequences of hyperspectral images are expected to be simultaneously affected by such phenomena when acquired on the same area at different time instants. Thus, we propose a hierarchical Bayesian model to simultaneously account for smooth and abrupt spectral variations affecting a set of multitemporal hyperspectral images to be jointly unmixed. This model assumes that smooth variations can be interpreted as the result of endmember variability, whereas abrupt variations are due to significant changes in the imaged scene (e.g., presence of outliers, additional endmembers, etc.). The parameters of this Bayesian model are estimated using samples generated by a Gibbs sampler according to its posterior. Performance assessment is conducted on synthetic data in comparison with state-of-the-art unmixing methods.

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“…Many physical phenomena can induce variations on the spectra of pure materials, be it a change in their physico-chemical composition, or the topography of the scene, which locally changes the incidence angle of the light and the viewing angle of the sensor. This phenomenon is referred to as endmember variability [11]- [13]. A physics-inspired model to explain illumination induced variability is the Extended Linear Mixing Model (ELMM) [14], which writes:…”

Section: Introductionmentioning

confidence: 99%

Relationships Between Nonlinear and Space-Variant Linear Models in Hyperspectral Image Unmixing

Drumetz

Ehsandoust

Chanussot

et al. 2017

IEEE Signal Process. Lett.

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Abstract-Hyperspectral image unmixing is a source separation problem whose goal is to identify the signatures of the materials present in the imaged scene (called endmembers), and to estimate their proportions (called abundances) in each pixel. Usually, the contributions of each material are assumed to be perfectly represented by a single spectral signature and to add up in a linear way. However, the main two limitations of this model have been identified as nonlinear mixing phenomena and spectral variability, i.e. the intraclass variability of the materials. The former limitation has been addressed by designing non linear mixture models, while the second can be dealt with by using (usually linear) space varying models. The typical example is a linear mixing model where the sources can vary from one pixel to the other. In this letter, we show that a recent variability model can also estimate the abundances of nonlinear mixtures to some extent. We make the theoretical connection between nonlinear models and this variability model, and confirm it with experiments on nonlinearly generated synthetic datasets.

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Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model

Cited by 172 publications

References 38 publications

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Unmixing multitemporal hyperspectral images accounting for smooth and abrupt variations

Relationships Between Nonlinear and Space-Variant Linear Models in Hyperspectral Image Unmixing

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