Texture analysis for oil spill segmentation based on X-band marine radar imagery

2017 ◽  
Author(s):  
Peng Liu ◽  
Ying Li ◽  
Jin Xu ◽  
Xueyuan Zhu ◽  
Haoran Qi
Keyword(s):  
Texture Analysis ◽  
Oil Spill ◽  
X Band ◽  
Radar Imagery ◽  
Marine Radar

Oil spill detection method using X-band marine radar imagery

2015 ◽  
Vol 9 (1) ◽  
pp. 095985 ◽  
Author(s):  
Xueyuan Zhu ◽  
Ying Li ◽  
Haiyang Feng ◽  
Bingxin Liu ◽  
Jin Xu
Keyword(s):  
Oil Spill ◽  
Detection Method ◽  
Oil Spill Detection ◽  
X Band ◽  
Radar Imagery ◽  
Marine Radar

Oil spill extraction by X-band marine radar using texture analysis and adaptive thresholding

2019 ◽  
Vol 10 (6) ◽  
pp. 583-589 ◽  
Author(s):  
Peng Liu ◽  
Ying Li ◽  
Jin Xu ◽  
Tong Wang
Keyword(s):  
Texture Analysis ◽  
Oil Spill ◽  
Adaptive Thresholding ◽  
X Band ◽  
Marine Radar

scholarly journals Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

2019 ◽  
Vol 11 (7) ◽  
pp. 756 ◽  
Author(s):  
Peng Liu ◽  
Ying Li ◽  
Bingxin Liu ◽  
Peng Chen ◽  
and Jin Xu
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection Method ◽  
Adaptive Thresholding ◽  
Oil Spill Detection ◽  
Radar Images ◽  
X Band ◽  
Marine Radar

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

Oil spill extraction from X-band marine radar images by power fitting of radar echoes

2021 ◽  
Vol 12 (4) ◽  
pp. 345-352
Author(s):  
Peng Liu ◽  
Yancheng Zhao ◽  
Bingxin Liu ◽  
Ying Li ◽  
Peng Chen
Keyword(s):  
Oil Spill ◽  
Radar Images ◽  
Radar Echoes ◽  
X Band ◽  
Marine Radar

A new modulation transfer function for ocean wave spectra retrieval from X-band marine radar imagery

2015 ◽  
Vol 33 (5) ◽  
pp. 1132-1141 ◽  
Author(s):  
Zhongbiao Chen ◽  
Biao Zhang ◽  
Yijun He ◽  
Zhongfeng Qiu ◽  
William Perrie
Keyword(s):  
Transfer Function ◽  
Modulation Transfer Function ◽  
Ocean Wave ◽  
Wave Spectra ◽  
Modulation Transfer ◽  
X Band ◽  
Radar Imagery ◽  
Marine Radar

Forecasting of oil-spill trajectories by using SCHISM and X-band radar

2018 ◽  
Vol 137 ◽  
pp. 566-581 ◽  
Author(s):  
Chi-Min Chiu ◽  
Ching-Jer Huang ◽  
Li-Chung Wu ◽  
Yinglong Joseph Zhang ◽  
Laurence Zsu-Hsin Chuang ◽  
...  
Keyword(s):  
Oil Spill ◽  
X Band
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