Journal
REMOTE SENSING
Volume 13, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/rs13152991
Keywords
Dirichlet process; infinite mixture models; Gamma distribution; variational inference; online setting; oil spill detection; synthetic aperture radar images
Categories
Funding
- Deanship of Scientific Research, Taif University, Kingdom of Saudi Arabia [1-441-137]
Ask authors/readers for more resources
This paper introduces a novel online nonparametric Bayesian analysis method, which can flexibly handle models with an infinite number of mixture components and has been successfully applied to oil spill detection in synthetic aperture radar (SAR) images.
In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case. In particular, we propose a novel online nonparametric Bayesian analysis method based on the infinite Gamma mixture model where the determination of the number of clusters is bypassed via an infinite number of mixture components. The proposed model is learned via an online extended variational Bayesian inference approach in a flexible way where the priors of model's parameters are selected appropriately and the posteriors are approximated effectively in a closed form. The online setting has the advantage to allow data instances to be treated in a sequential manner, which is more attractive than batch learning especially when dealing with massive and streaming data. We demonstrated the performance and merits of the proposed statistical framework with a challenging real-world application namely oil spill detection in synthetic aperture radar (SAR) images.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available