Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

2013 ◽  
Vol 4 (1) ◽  
pp. 84-111
Author(s):  
Kufre Bassey ◽  
Polycarp Chigbu
Keyword(s):  
Remote Sensing ◽  
Environmental Science ◽  
Oil Spills ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Marine Oil ◽  
Detection And Estimation ◽  
Number Of False Alarms ◽  
Marine Oil Spills ◽  
Remote Sensing Techniques

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

Remote sensing of marine oil spills and its applications

2008 ◽  
Author(s):  
Ying Li ◽  
Long Ma ◽  
Shui-ming Yu ◽  
Chuan-long Li ◽  
Qi-jun Li
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Marine Oil ◽  
Marine Oil Spills

scholarly journals Ultraviolet remote sensing of marine oil spills: A new approach of HaiYang- 1C satellite

2021 ◽  
Author(s):  
Yingcheng Lu ◽  
Ziyi Suo ◽  
jianqiang liu ◽  
Jing Ding ◽  
Dayi Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
New Approach ◽  
Marine Oil ◽  
Marine Oil Spills ◽  
Ultraviolet Remote Sensing

scholarly journals Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

2021 ◽  
Vol 14 (1) ◽  
pp. 157
Author(s):  
Zongchen Jiang ◽  
Jie Zhang ◽  
Yi Ma ◽  
Xingpeng Mao
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Heavy Oils ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Moment Estimation ◽  
Marine Oil Spills ◽  
Oil Films

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

Response to marine oil spills

1988 ◽  
Vol 19 (6) ◽  
pp. 297
Author(s):  
R.A.A. Blackman
Keyword(s):  
Oil Spills ◽  
Marine Oil ◽  
Marine Oil Spills

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

The application of a carrier-based bioremediation strategy for marine oil spills

2014 ◽  
Vol 84 (1-2) ◽  
pp. 339-346 ◽  
Author(s):  
Petra J. Sheppard ◽  
Keryn L. Simons ◽  
Eric M. Adetutu ◽  
Krishna K. Kadali ◽  
Albert L. Juhasz ◽  
...  
Keyword(s):  
Oil Spills ◽  
Marine Oil ◽  
Marine Oil Spills
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