Unmixing compositional data with Bayesian techniques
نویسنده
چکیده
A general problem in compositional data analysis is the unmixing of a composition into a series of pure endmembers. In its most complex version, one does neither know the composition of these endmembers, nor their relative contribution to each observed composition. The problem is particularly cumbersome if the number of endmembers is larger than the number of observed components. This contribution proposes a possible solution of this under-determined problem. The proposed method starts assuming that the endmember composition is known. Then, a geometric characterization of the problem allows to find the set of possible endmember proportions compatible with the observed composition. Within this set any solution may be valid, but some are more likely than other. To use this idea and choose the “most likely” solution in each case, the problem can be tackled with Bayesian Markov-Chain Monte-Carlo techniques. Finally, once we are familiar with MCMC, it is quite straightforward to allow the endmember compositions to randomly vary, and use the same MCMC to estimate the endmember composition most compatible with the studied data.
منابع مشابه
An Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data
The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum i...
متن کاملBlind Unmixing of Linear Mixtures Using a Hierarchical Bayesian
This paper addresses the problem of spectral unmixing when positivity and additivity constraints are imposed on the mixing coefficients. A hierarchical Bayesian model is introduced to satisfy these two constraints. A Gibbs sampler is then proposed to generate samples distributed according to the posterior distribution of the unknown parameters associated to this Bayesian model. Simulation resul...
متن کاملBayesian Algorithm for Unsupervised Unmixing of Hyperspectral Images Using a Post-nonlinear Model
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components contaminated by an additive white Gaussian noise. The nonlinear effects are approximated by a polynomial leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm is proposed to esti...
متن کاملArea-Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization
To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed method, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of t...
متن کاملAnalysis of Hyperspectral Imagery for Oil Spill Detection Using SAM Unmixing Algorithm Techniques
Oil spill is one of major marine environmental challenges. The main impacts of this phenomenon are preventing light transmission into the deep water and oxygen absorption, which can disturb the photosynthesis process of water plants. In this research, we utilize SpecTIR airborne sensor data to extract and classify oils spill for the Gulf of Mexico Deepwater Horizon (DWH) happened in 2010. For t...
متن کامل