Model based oil spill detection using polarimetric SAR

As a major marine pollution source, oil spills largely threaten the sustainability of the coastal environment. Polarimetric synthetic aperture radar remote sensing has become a promising approach for marine oil spill detection since it could effectively separate crude oil and biogenic look-alikes. However, on the sea surface, the signal to noise ratio of Synthetic Aperture Radar (SAR) backscatter is usually low, especially for cross-polarized channels. In practice, it is necessary to combine the refined detail of slick-sea boundary derived from the co-polarized channel and the highly accurate crude slick/look-alike classification result obtained based on the polarimetric information. In this paper, the architecture for oil spill detection based on polarimetric SAR is proposed and analyzed. The performance of different polarimetric SAR filters for oil spill classification are compared. Polarimetric SAR features are extracted and taken as the input of Staked Auto Encoder (SAE) to achieve high accurate classification between crude oil, biogenic slicks, and clean sea surface. A post-processing method is proposed to combine the classification result derived from SAE and the refined boundary derived from VV channel power image based on special density thresholding (SDT). Experiments were conducted on spaceborne fully polarimetric SAR images where both crude oil and biogenic slicks were presented on the sea surface.

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A Model Based Approach to Radar Oil Spill Detection

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-2014.1.2228 ◽

2014 ◽

Vol 2014 (1) ◽

pp. 2228-2241

Author(s):

Torstein Pedersen ◽

Javier Perez ◽

Jos Van Heseen

Keyword(s):

Oil Spill ◽

Oil Spills ◽

Radar Image ◽

Monitoring Systems ◽

Multiple Sources ◽

Threshold Detector ◽

Ir Camera ◽

Oil Spill Detection ◽

Model Based ◽

Primary Driver

ABSTRACT A typical oil spill recovery vessel has been historically outfitted with an oil spill detection (OSD) radar. During an oil spill recovery operation, there is a dedicated operator who is responsible for interpreting information from the radar image. Industry developments over the last several years now require that an OSD radar automatically detect and track an oil spill. There are two primary needs driving this development. The first is that OSD systems and operations are becoming more sophisticated; automatic OSD aids for a more efficient oil spill operation where an operator's attention may be directed to a potential spill. The automatic OSD also aids a multi-sensor system; one such example is where an OSD radar is used to steer an IR camera to a candidate spill for more detailed evaluation or validation. The other primary driver for automatic OSD is for monitoring systems, which serve for early warning. Monitoring systems may be found along coastal installations or oil platforms. The automatic spill detection functionality of an OSD system may be implemented in different levels of sophistication. Perhaps the simplest configuration is one that uses fixed thresholds relative to the image for alarming whether a region in a radar image is a spill or not. The benefit of simple threshold detector is that it is easy to implement in software. The weakness is that it is prone to both lower overall detection rate and high false alarm rate. A more robust automatic spill detection method is one that treats it as an image-processing problem. The paper here presents a model based OSD. Generation of confidence maps is central to the method and provides an indication of the likelihood of oil. Inputs to the confidence maps come from multiple sources, several of which are based on uniquely constructed models. Among these is a histogram comparator, which scans a radar image and compares the data to reference models from real oil spills. A discussion of the methods used focuses on (a) the necessary steps prior to the confidence map construction, (b) how the confidence maps are layered with inputs, (c) how the information in the confidence maps is transitioned into the detection of oil, (d) and finally alarming.

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Oil spill detection using refined convolutional neural network based on quad-polarimetric SAR images

2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) ◽

10.1109/icemi46757.2019.9101622 ◽

2019 ◽

Author(s):

Zhang Jin ◽

Luo Qingli ◽

Li Yu ◽

Feng Hao ◽

Wei Jujie

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Oil Spill ◽

Polarimetric Sar ◽

Sar Images ◽

Oil Spill Detection

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

Remote Sensing ◽

10.3390/rs11040451 ◽

2019 ◽

Vol 11 (4) ◽

pp. 451 ◽

Cited By ~ 8

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°.

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