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.

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 Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology

2019 ◽  
Vol 8 (4) ◽  
pp. 181 ◽  
Author(s):  
Xueyuan Zhu ◽  
Ying Li ◽  
Qiang Zhang ◽  
Bingxin Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Film Thickness ◽  
Oil Spill ◽  
Spectral Characteristics ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Oil Film ◽  
Remote Sensing Technology ◽  
Sensing Technology

Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm’s classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified.

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 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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