50 research outputs found
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
This paper studies a fully Bayesian algorithm for endmember extraction and
abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral
image is decomposed as a linear combination of pure endmember spectra following
the linear mixing model. The estimation of the unknown endmember spectra is
conducted in a unified manner by generating the posterior distribution of
abundances and endmember parameters under a hierarchical Bayesian model. This
model assumes conjugate prior distributions for these parameters, accounts for
non-negativity and full-additivity constraints, and exploits the fact that the
endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is
proposed to overcome the complexity of evaluating the resulting posterior
distribution. This sampler generates samples distributed according to the
posterior distribution and estimates the unknown parameters using these
generated samples. The accuracy of the joint Bayesian estimator is illustrated
by simulations conducted on synthetic and real AVIRIS images
Implementation strategies for hyperspectral unmixing using Bayesian source separation
Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach
for hyperspectral data unmixing, where numerical non-negativity of spectra and
abundances has to be ensured, such in remote sensing. Moreover, it is sensible
to impose a sum-to-one (full additivity) constraint to the estimated source
abundances in each pixel. Even though non-negativity and full additivity are
two necessary properties to get physically interpretable results, the use of
BPSS algorithms has been so far limited by high computation time and large
memory requirements due to the Markov chain Monte Carlo calculations. An
implementation strategy which allows one to apply these algorithms on a full
hyperspectral image, as typical in Earth and Planetary Science, is introduced.
Effects of pixel selection, the impact of such sampling on the relevance of the
estimated component spectra and abundance maps, as well as on the computation
times, are discussed. For that purpose, two different dataset have been used: a
synthetic one and a real hyperspectral image from Mars.Comment: 10 pages, 6 figures, submitted to IEEE Transactions on Geoscience and
Remote Sensing in the special issue on Hyperspectral Image and Signal
Processing (WHISPERS
NON-MATRIX FACTORIZATION FOR BLIND IMAGE SEPARATION
Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding fractions from the mixture, nonnegative matrix factions ( NMF ) is suitable as a candidate for the linear spectral mixture mode, has been applied to the unmixing hyperspectral data. Unfortunately, the local minima is cause by the nonconvexity of the objective function makes the solution nonunique, thus only the nonnegativity constraint is not sufficient enough to lead to a well define problems. Therefore, two inherent characteristic of hyperspectal data, piecewise smoothness ( both temporal and spatial ) of spectral data and sparseness of abundance fraction of every material, are introduce to the NMF. The adaptive potential function from discontinuity adaptive Markov random field model is used to describe the smoothness constraint while preserving discontinuities is spectral data. At the same time two NMF algorithms, non smooth NMS and NMF with sparseness constraint, are used to quantify the degree of sparseness of material abundances. Experiment using the synthetic and real data demonstrate the proposed algorithms provides an effective unsupervised technique for hyperspectial unmixing
Non-negative matrix factorization for blind image separation
Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding fractions from the mixture, nonnegative matrix factions ( NMF ) is suitable as a candidate for the linear spectral mixture mode, has been applied to the unmixing hyperspectral data. Unfortunately, the local minima is cause by the nonconvexity of the objective function makes the solution nonunique, thus only the nonnegativity constraint is not sufficient enough to lead to a well define problems. Therefore, two inherent characteristic of hyperspectal data, piecewise smoothness ( both temporal and spatial ) of spectral data and sparseness of abundance fraction of every material, are introduce to the NMF. The adaptive potential function from discontinuity adaptive Markov random field model is used to describe the smoothness constraint while preserving discontinuities is spectral data. At the same time two NMF algorithms, non smooth NMS and NMF with sparseness constraint, are used to quantify the degree of sparseness of material abundances. Experiment using the synthetic and real data demonstrate the proposed algorithms provides an effective unsupervised technique for hyperspectial unmixin