Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

@article{BioucasDias2012HyperspectralUO,
  title={Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches},
  author={Jos{\'e} M. Bioucas-Dias and Antonio J. Plaza and Nicolas Dobigeon and Mario Parente and Qian Du and Paul D. Gader and Jocelyn Chanussot},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2012},
  volume={5},
  pages={354-379}
}
  • J. Bioucas-Dias, A. Plaza, J. Chanussot
  • Published 28 February 2012
  • Environmental Science, Mathematics
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical… 
Endmember Variability in hyperspectral image unmixing
The fine spectral resolution of hyperspectral remote sensing images allows an accurate analysis of the imaged scene, but due to their limited spatial resolution, a pixel acquired by the sensor is
Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability
TLDR
The proposed algorithm to unmix hyperspectral data using a recently proposed extended LMM outperforms other methods aimed at addressing spectral variability, and can provide an accurate estimation of endmember variability along the scene because of the scaling factors estimation.
Bayesian Nonparametric Unmixing of Hyperspectral Images
TLDR
This work proposes a Bayesian nonparametric framework that jointly estimates the number of endmembers, the endmembers itself, and their abundances, by making use of the Indian Buffet Process as a prior for the end members.
Comparative Analysis of Unmixing Algorithms Using Synthetic Hyperspectral Data
TLDR
A large-scale comparison of endmember extraction algorithms is presented and it is shown that MVSA and SISAL demonstrate robust performance to the changes in the size of the scene.
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing and Its Applications
  • Lei Tong
  • Environmental Science, Mathematics
  • 2016
TLDR
Three different constraints for the NMF based hyperspectral unmixing method are introduced, which minimizes the differences between the spectral signatures of endmembers being estimated in the image data and the standard signa- tures of known endmembers extracted from a library or detected from the ground.
BAYESIAN SUBSPACE ESTIMATION USING SPARSE PROMOTING PRIOR
TLDR
The goal is to avoid an arbitrary thresholding of eigenvalues as often done for PCA, and derive an empirical posterior distribution of bases of the latent subspace, where coefficients, e.g. projections, have been marginalized out.
Blind Unmixing of Hyperspectral Remote Sensing Data: A New Geometrical Method Based on a Two-Source Sparsity Constraint
TLDR
The proposed linear and geometrical sparse-based, blind (or unsupervised) unmixing method is applied to realistic synthetic images and is shown to outperform various methods from the literature.
Nonlinear spectral unmixing
MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression
TLDR
A two-step algorithm aimed at mitigating the aforementioned limitations of sparse unmixing and the effectiveness of the proposed approach, termed MUSIC-CSR, is extensively validated using both simulated and real hyperspectral data sets.
Sparse methods for hyperspectral unmixing and image fusion
TLDR
This thesis proposes novel methods for hyperspectral unmixing using sparse approximation techniques and external spectral dictionaries, which unlike traditional least squares-based methods, do not require pure material spectrum selection step and are thus able to simultaneously estimate the underlying active materials along with their respective abundances.
...
...

References

SHOWING 1-10 OF 253 REFERENCES
Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches
TLDR
An overview of the principal research directions in hyperspectral unmixing is presented and what physical or mathematical problems are involved are described and state-of-the-art algorithms to address these problems are summarized.
Sparse Unmixing of Hyperspectral Data
TLDR
The experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra.
Recent Developments in Endmember Extraction and Spectral Unmixing
TLDR
This chapter provides an overview of existing techniques for spectral unmixing and endmember extraction, with particular attention paid to recent advances in the field such as the incorporation of spatial information into the endmember searching process, or the use of nonlinear mixture models for fractional abundance characterization.
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing
TLDR
The total variation (TV) regularization to the classical sparse regression formulation is included, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV.
Hyperspectral unmixing algorithm via dependent component analysis
TLDR
This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene and the corresponding abundance fractions at each pixel, thus enforcing the constraints on abundance fractions imposed by the acquisition process.
Robust hyperspectral data unmixing with spatial and spectral regularized NMF
  • A. Huck, M. Guillaume
  • Environmental Science, Mathematics
    2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
  • 2010
TLDR
This paper considers the problem of unsupervised hyperspectral data unmixing under the linear spectral mixing model assumption (LSMM), and considers the Non-negative Matrix Factorization (NMF), which is degenerated on its own, but has the advantage of low complexity and the ability to easily include physical constraints.
Survey of geometric and statistical unmixing algorithms for hyperspectral images
  • M. Parente, A. Plaza
  • Environmental Science
    2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
  • 2010
TLDR
This paper gives a comprehensive enumeration of the unmixing methods used in practice, because of their implementation in widely used software packages, and those published in the literature, according to the basic computational approach followed by the algorithms.
Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units
TLDR
This paper develops an implementation of the full hyperspectral unmixing chain on commodity graphics processing units (GPUs), and has been implemented, using the CUDA (compute device unified architecture), and tested on three different GPU architectures.
Spectral and Spatial Complexity-Based Hyperspectral Unmixing
  • Sen Jia, Y. Qian
  • Environmental Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2007
TLDR
A complexity-based BSS algorithm is introduced, which studies the complexity of sources instead of the independence, and a strict theoretic interpretation is given, showing that the complexity- based BSS is very suitable for hyperspectral unmixing.
Spatial/spectral endmember extraction by multidimensional morphological operations
TLDR
A new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner.
...
...