Multi-feature Fusion for PolSAR Image Classification of Oil Slick Thickness

DOI

10.2991/icemc-17.2017.44How to use a DOI?

Keywords

Polarimetric Synthetic Aperture Radar (PolSAR); Multi-feature fusion; Oil slick; Image classification

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/icemc-17.2017.44
ID  - Shiyi2016/06
ER  -