Bayesian unmixing using sparse dirichlet prior with polynomial post-nonlinear mixing model
نویسندگان
چکیده
منابع مشابه
Bayesian 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 کاملBayesian Subspace Estimation Using Sparse Promoting Prior
Hyperspectral sensors record the light intensity beyond the visible spectra in hundreds of narrow contiguous bands. Images are characterized by a high spectral resolution but a low spatial precision due to sensors constraints. A crucial step called unmixing consists of decomposing each pixel as a combination of pure spectra, called endmembers. Endmembers act as fingerprints, improving the abili...
متن کاملIntroducing of Dirichlet process prior in the Nonparametric Bayesian models frame work
Statistical models are utilized to learn about the mechanism that the data are generating from it. Often it is assumed that the random variables y_i,i=1,…,n ,are samples from the probability distribution F which is belong to a parametric distributions class. However, in practice, a parametric model may be inappropriate to describe the data. In this settings, the parametric assumption could be r...
متن کاملOn the Dirichlet Prior and Bayesian Regularization
Motivation & Previous Work: A common objective in learning a model from data is to recover its network structure, while the model parameters are of minor interest. For example, we may wish to recover regulatory networks from high-throughput data sources. Regularization is essential when learning from finite data sets. It provides not only smoother estimates of the model parameters compared to m...
متن کاملUnmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior
Hyperspectral imaging can be used in assessing the quality of foods by decomposing the image into constituents such as protein, starch, and water. Observed data can be considered a mixture of underlying characteristic spectra (endmembers), and estimating the constituents and their abundances requires efficient algorithms for spectral unmixing. We present a Bayesian spectral unmixing algorithm e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEJ Transactions on Electrical and Electronic Engineering
سال: 2018
ISSN: 1931-4973
DOI: 10.1002/tee.22849