Multi-feature Fusion for PolSAR Image Classification of Oil Slick Thickness
- https://doi.org/10.2991/icemc-17.2017.44How to use a DOI?
- Polarimetric Synthetic Aperture Radar (PolSAR); Multi-feature fusion; Oil slick; Image classification
The oil slick outline and the information of thickness are important indicators of estimating oil spill. How to estimate the oil slick thickness quickly is a hot research topic. In this paper, multi-feature fusion strategy is used to design classifier based on the potential correlation between Polarimetric Synthetic Aperture Radar (PolSAR) characteristics and oil slick thickness. Taking into account the correlation between polarization characteristics, Mahalanobis distance is used to optimize initial cluster center of fuzzy C-means clustering and then, the estimation of oil slick thickness is carried out. The algorithm is proved to be effective by classifying the oil slick thickness of two groups of PolSAR oil spill data in Mexico Bay.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hong Shiyi AU - Guo Hao AU - An Jubai PY - 2016/06 DA - 2016/06 TI - Multi-feature Fusion for PolSAR Image Classification of Oil Slick Thickness BT - Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017) PB - Atlantis Press SP - 220 EP - 223 SN - 2352-538X UR - https://doi.org/10.2991/icemc-17.2017.44 DO - https://doi.org/10.2991/icemc-17.2017.44 ID - Shiyi2016/06 ER -