A stochastic model for oil spill detection in marine environment with SAR data

2018 ◽  
Author(s):  
Flavio Parmiggiani ◽  
Laura L. Álvarez-Hernández ◽  
Miguel Moctezuma-Flores
Keyword(s):  
Stochastic Model ◽  
Oil Spill ◽  
Marine Environment ◽  
Oil Spill Detection ◽  
Sar Data

scholarly journals Integral Solution for Oil Spill Detection using SAR Data

2007 ◽  
Author(s):  
Olga Nickolaevna Gershenzon ◽  
Vladimir Eugenyevich Gershenzon ◽  
Sergey Vladimirovich Osheyko
Keyword(s):  
Oil Spill ◽  
Integral Solution ◽  
Oil Spill Detection ◽  
Sar Data

One Operational Visual Method for Oil Spill Detection by SAR Image

2015 ◽  
Vol 742 ◽  
pp. 208-211
Author(s):  
Peng Chen ◽  
Kai Guo Fan ◽  
Yan Zhen Gu ◽  
Ke Xu
Keyword(s):  
Oil Spill ◽  
Marine Environment ◽  
Sar Image ◽  
Environment Protection ◽  
Visual Method ◽  
Oil Spill Detection

SAR has been proven to be a useful tool for ocean oil spill detection due to its large coverage, independence of the day and night cycle and all-weather capability. In this paper, one operational visual method for oil spill detection using SAR image was performed and the oil spill key information, such as the location and coverage, has also been demonstrated. The results show that the operational visual method of oil spill detection by SAR image will play an important role in the marine environment protection.

Modification of CFAR Algorithm for Oil Spill Detection from SAR Data

2014 ◽  
Vol 21 (2) ◽  
pp. 163-174 ◽  
Author(s):  
Siyuan Wang ◽  
Xingyu Fu ◽  
Yan Zhao ◽  
Hui Wang
Keyword(s):  
Oil Spill ◽  
Oil Spill Detection ◽  
Sar Data

scholarly journals Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

2019 ◽  
Vol 11 (4) ◽  
pp. 451 ◽  
Author(s):  
Shengwu Tong ◽  
Xiuguo Liu ◽  
Qihao Chen ◽  
Zhengjia Zhang ◽  
Guangqi Xie
Keyword(s):  
Wind Speed ◽  
Random Forest ◽  
Oil Spill ◽  
The Self ◽  
Detection Accuracy ◽  
Self Similarity ◽  
Polarimetric Sar ◽  
Similarity Parameter ◽  
Oil Spill Detection ◽  
Sar Data

Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.

Target decomposition theory in oil spill detection from SAR data

2015 ◽  
Vol 7 (3) ◽  
pp. 264-281
Author(s):  
Ali Akbar Matkan ◽  
Mohammad Hajeb ◽  
Zeinab Azarakhsh
Keyword(s):  
Oil Spill ◽  
Decomposition Theory ◽  
Oil Spill Detection ◽  
Sar Data

A new stochastic model for oil spill observation by means of single-look SAR data

2006 ◽  
Author(s):  
Maurizio Migliaccio ◽  
Giuseppe Ferrara ◽  
Attilio Gambardella ◽  
Ferdinando Nunziata
Keyword(s):  
Stochastic Model ◽  
Oil Spill ◽  
Sar Data
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