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

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

scholarly journals Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data

2015 ◽  
Vol 12 (3) ◽  
pp. 1263-1289 ◽  
Author(s):  
M. Marghany
Keyword(s):  
Evolutionary Algorithm ◽  
Oil Spill ◽  
Satellite Data ◽  
Marine Mammals ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Design Tool ◽  
Multi Objective ◽  
Oil Spill Detection ◽  
Sar Data

Abstract. Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.

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