Abstract
This paper describes a novel framework to segment elongated strips of marine oil spill based on a cooperative model. We construct a higher-order energy function which characterizes data consistency and smoothness of varying orders in an integrated framework. We model the data consistency in terms of a finite Gamma mixture and then estimate its parameter values using an expectation maximization (EM) algorithm. In order to characterise the smoothness term we exploit a cooperative model which not only penalizes the length but also addresses the diversity of the object boundary. This advantage makes our framework more effective for segmenting elongated strip oil spill than traditional energy minimization methods such as graph cuts. Our framework is further evaluated using practical SAR images of oil spill recordings and its effectiveness is validated.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 |
| Publisher | IEEE |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-4799-6731-5 |
| DOIs | |
| Publication status | Published - 2014 |
Keywords
- crude oil
- expectation-maximisation algorithm
- graph theory
- image segmentation
- radar imaging
- synthetic aperture radar
- EM algorithm
- cooperative model
- data consistency
- elongated strip oil spill segmentation
- energy function
- energy minimization methods
- expectation maximization algorithm
- finite Gamma mixture
- graph cuts
- integrated framework
- object boundary
- oil spill recordings
- practical SAR images
- smoothness
- Biological system modeling
- Computational modeling
- Mathematical model
- Strips
- Vegetation
- Oil spill segmentation
