@inproceedings{c2a6b3a5e2d54c31b78ff218e25ffbf4,
title = "Study on extraction methods of ocean surface oil spill using HJ-CCD data",
abstract = "Oil spill is one of the most common ocean environmental disasters. Rapid and accurate access to the oil spill information has great significances to dynamic monitoring, conservation and sustainable use of the ocean. HJ-1 is a new satellite platform designed for environmental disasters. However, multi-spectral images obtained from HJ-1 have fewer spectral bands and the accuracy of acquiring the oil spill coverage only by spectral information is low. In this paper, oil spill occurred in Gulf of Mexico was selected as the research object. The texture characteristics which affect oil spill identification were extracted by Gray Level Co-occurrence Matrix (GLCM) and a decision tree method combined spectral characteristics with texture characteristics was established to extract the oil spill. And comparative analysis with the extraction result of Maximum Likelihood Classification (MLC) was performed. Results showed that, compared with MLC, the decision tree classification raised the user accuracy and the producer accuracy of extracting oil spill by 11.85% and 4.28%, respectively.",
keywords = "Decision tree, Gulf of Mexico, HJ-1, Oil spill, Texture characteristic",
author = "Yingying Gai and Bin Zhou and Yuanfang Sun and Yan Zhou and WenWen Li",
year = "2013",
month = jan,
day = "1",
language = "English (US)",
isbn = "9789078677772",
series = "International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2013",
publisher = "Atlantis Press",
pages = "756--759",
booktitle = "International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2013",
note = "2013 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2013 ; Conference date: 26-07-2013 Through 28-07-2013",
}