Oil Spill Detection in SAR Images Using Online Extended Variational Learning of Dirichlet Process Mixtures of Gamma Distributions
Open Access
- 29 July 2021
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 13 (15), 2991
- https://doi.org/10.3390/rs13152991
Abstract
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.This publication has 53 references indexed in Scilit:
- A finite mixture model for simultaneous high-dimensional clustering, localized feature selection and outlier rejectionExpert Systems with Applications, 2012
- One-class classification for oil spill detectionPattern Analysis and Applications, 2009
- Investigation of genetic algorithms contribution to feature selection for oil spill detectionInternational Journal of Remote Sensing, 2009
- Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification AlgorithmsSensors, 2008
- Automatic detection and tracking of oil spills in SAR imagery with level set segmentationInternational Journal of Remote Sensing, 2008
- Automatic identification of oil spills on satellite imagesEnvironmental Modelling & Software, 2006
- Variational inference for Dirichlet process mixturesBayesian Analysis, 2006
- Oil spill detection by satellite remote sensingRemote Sensing of Environment, 2005
- Online Model Selection Based on the Variational BayesNeural Computation, 2001
- Automatic detection of oil spills in ERS SAR imagesIEEE Transactions on Geoscience and Remote Sensing, 1999