Novel Electro-Optic Imaging Technologies for Day/Night Oil Spill Detection

2014 ◽  
Vol 2014 (1) ◽  
pp. 299609
Author(s):  
Toomas H. Allik ◽  
Roberta E. Dixon ◽  
Lenard V. Ramboyong ◽  
Mark Roberts ◽  
Thomas J. Soyka ◽  
...  
Keyword(s):  
Oil Spill ◽  
Infrared Imaging ◽  
Imaging Techniques ◽  
Spectral Characteristics ◽  
Laboratory Data ◽  
Field Tests ◽  
Light Level ◽  
Test Facility ◽  
Imaging Technologies ◽  
Oil Spill Detection

Joint program between the U.S. Departments of the Interior and Defense to bring knowledge, expertise and military, low-light level and hyperspectral imaging technologies to remote oil spill detection. Program emphasis is to determine remote infrared imaging techniques for the quantification of oil spill thickness. Spectral characteristics of various crude oils in the SWIR (1–2 microns), MWIR (3–5 microns) and LWIR (8–12 microns) were measured. Analysis of laboratory data and Deepwater Horizon hyperspectral imagery showed the utility of the SWIR region to detect crude oil and emulsions. We have evaluated two SWIR wavelengths (1200 nm and 1250 nm) for thickness assessment. An infrared, 3-color imager is discussed along with field tests at the BSEE's Ohmsett test facility.

scholarly journals Oil Spill Detection Using Machine Learning and Infrared Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4090
Author(s):  
Thomas De Kerf ◽  
Jona Gladines ◽  
Seppe Sels ◽  
Steve Vanlanduit
Keyword(s):  
Neural Network ◽  
Oil Spill ◽  
Infrared Imaging ◽  
Oil Spills ◽  
Infrared Images ◽  
Ir Camera ◽  
Oil Spill Detection ◽  
Imaging Camera ◽  
Rgb Images ◽  
Trained Network

The detection of oil spills in water is a frequently researched area, but most of the research has been based on very large patches of crude oil on offshore areas. We present a novel framework for detecting oil spills inside a port environment, while using unmanned areal vehicles (UAV) and a thermal infrared (IR) camera. This framework is split into a training part and an operational part. In the training part, we present a process for automatically annotating RGB images and matching them with the IR images in order to create a dataset. The infrared imaging camera is crucial to be able to detect oil spills during nighttime. This dataset is then used to train on a convolutional neural network (CNN). Seven different CNN segmentation architectures and eight different feature extractors are tested in order to find the best suited combination for this task. In the operational part, we propose a method to have a real-time, onboard UAV oil spill detection using the pre-trained network and a low power interference device. A controlled experiment in the port of Antwerp showed that we are able to achieve an accuracy of 89% while only using the IR camera.

scholarly journals Remote System for Oil Spill Detection Based on ZigBee and GIS

2016 ◽  
Vol 12 (11) ◽  
pp. 4 ◽  
Author(s):  
Huishu Hou ◽  
Guojun Zhang
Keyword(s):  
Oil Spill ◽  
Delay Time ◽  
System Performance ◽  
Spectral Characteristics ◽  
Oil Film ◽  
Oil Spill Detection ◽  
Simulation Results ◽  
Remote System ◽  
Marine Accidents

In order to solve the problem of long delay time and low data precision, a remote system for oil spill detection based on ZigBee and GIS is designed in this paper. This paper analytically summarizes the results obtained from surveying spectral characteristics of spill oil film at sea. M along use of AVHRR and TM data, the image of oil spill in several marine accidents has been processed and identified, and the place and area of oil spill in obtaining images are the same as investigation in the scene of accidents. Simulation results show that the proposed system which bases on the ZigBee and GIS can thus improve overall system performance substantially.

scholarly journals Oil Spill Detection Using Fluorometric Sensors: Laboratory Validation and Implementation to a FerryBox and a Moored SmartBuoy

2021 ◽  
Vol 8 ◽  
Author(s):  
Siim Pärt ◽  
Harri Kankaanpää ◽  
Jan-Victor Björkqvist ◽  
Rivo Uiboupin
Keyword(s):  
Real Time ◽  
Oil Spill ◽  
Oil Spills ◽  
Autonomous Systems ◽  
Field Tests ◽  
Primary Source ◽  
Visual Surveillance ◽  
Fluorescence Signal ◽  
Time Data ◽  
Oil Spill Detection

A large part of oil spills happen near busy marine fairways. Presently, oil spill detection and monitoring are mostly done with satellite remote sensing algorithms, or with remote sensors or visual surveillance from aerial vehicles or ships. These techniques have their drawbacks and limitations. We evaluated the feasibility of using fluorometric sensors in flow-through systems for real-time detection of oil spills. The sensors were capable of detecting diesel oil for at least 20 days in laboratory conditions, but the presence of CDOM, turbidity and algae-derived substances substantially affected the detection capabilities. Algae extract was observed to have the strongest effect on the fluorescence signal, enhancing the signal in all combinations of sensors and solutions. The sensors were then integrated to a FerryBox system and a moored SmartBuoy. The field tests support the results of the laboratory experiments, namely that the primary source of the measured variation was the presence of interference compounds. The 2 month experiments data did not reveal peaks indicative of oil spills. Both autonomous systems worked well, providing real-time data. The main uncertainty is how the sensors' calibration and specificity to oil, and the measurement depth, affects oil detection. We recommend exploring mathematical approaches and more advanced sensors to correct for natural interferences.

Application of Ground-based and Air/Spaceborne Radars for Oil Spill Detection in Sea Areas

1997 ◽  
Vol 51 (1) ◽  
pp. 1-8
Author(s):  
Ye. N. Belov ◽  
V. B. Yefimov ◽  
A. I. Kalmykov ◽  
I. A. Kalmykov ◽  
A. S. Kurekin ◽  
...  
Keyword(s):  
Oil Spill ◽  
Oil Spill Detection

scholarly journals Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5176
Author(s):  
Guannan Li ◽  
Ying Li ◽  
Bingxin Liu ◽  
Peng Wu ◽  
Chen Chen
Keyword(s):  
Computational Complexity ◽  
Synthetic Aperture Radar ◽  
Oil Spill ◽  
Similarity Measures ◽  
Synthetic Aperture ◽  
Matching Method ◽  
Polarimetric Synthetic Aperture Radar ◽  
Spectral Similarity ◽  
Oil Spill Detection ◽  
Aperture Radar

Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity.

scholarly journals Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks

2021 ◽  
Vol 13 (15) ◽  
pp. 3037
Author(s):  
Huy Hoa Huynh ◽  
Jaehung Yu ◽  
Lei Wang ◽  
Nam Hoon Kim ◽  
Bum Han Lee ◽  
...  
Keyword(s):  
3D Model ◽  
Imaging Techniques ◽  
Iron Hydroxide ◽  
Spectral Characteristics ◽  
Short Wave ◽  
Rock Classification ◽  
3D Geological Modeling ◽  
Logistic Regression Models ◽  
Rock Formations ◽  
Absorption Features

This paper demonstrates an integrative 3D model of short-wave infrared (SWIR) hyperspectral mapping and unmanned aerial vehicle (UAV)-based digital elevation model (DEM) for a carbonate rock outcrop including limestone and dolostone in a field condition. The spectral characteristics in the target outcrop showed the limestone well coincided with the reference spectra, while the dolostone did not show clear absorption features compared to the reference spectra, indicating a mixture of clay minerals. The spectral indices based on SWIR hyperspectral images were derived for limestone and dolostone using aluminum hydroxide (AlOH), hydroxide (OH), iron hydroxide (FeOH), magnesium hydroxide (MgOH) and carbonate ion (CO32−) absorption features based on random forest and logistic regression models with an accuracy over 87%. Given that the indices were derived from field data with consideration of commonly occurring geological units, the indices have better applicability for real world cases. The integrative 3D geological model developed by co-registration between hyperspectral map and UAV-based DEM using best matching SIFT descriptor pairs showed the 3D rock formations between limestone and dolostone. Moreover, additional geological information of the outcrop was extracted including thickness, slope, rock classification, strike, and dip.

scholarly journals Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1607
Author(s):  
Guannan Li ◽  
Ying Li ◽  
Yongchao Hou ◽  
Xiang Wang ◽  
Lin Wang
Keyword(s):  
Synthetic Aperture Radar ◽  
Oil Spill ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Data ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Oil Slick ◽  
Feature Parameters ◽  
Aperture Radar

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.

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