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Self-supervised learning-based oil spill detection of hyperspectral images

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Science China(Technological Sciences) 2022年第4期65卷 793-801页

作者： DUAN PuHong XIE ZhuoJun KANG XuDong LI ShuTao College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha 410082China

oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted ***,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training *** solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection ***,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised ***,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic ***,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed *** performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.

关键词： hyperspectral image self-supervised learning data augmentation oil spill detection contrastive loss

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A new oil spill detection algorithm based on Dempster-Shafer evidence theory

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Journal of Oceanology and Limnology 2022年第2期40卷 456-469页

作者： Tianlong ZHANG Jie GUO Chenqi XU Xi ZHANG Chuanyuan WANG Baoquan LI Yantai Institute of Coastal Zone Research(YIC) Chinese Academy of Sciences(CAS)CAS Key Laboratory of Coastal Environmental Processes and Ecological RemediationYantai 264003China Shandong Key Laboratory of Coastal Environmental Processes Yantai Institute of Coastal Zone ResearchChinese Academy of SciencesYantai 264003China Center for Ocean Mega-Science Chinese Academy of SciencesQingdao 266071China University of the Chinese Academy of Sciences Beijing 100049China Frist Institute of Oceanography(FIO) Ministry of Natural Resources(MNR)Qingdao 266061China

Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill *** oil spill detection algorithms have been implemented based on these *** environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental *** distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was *** process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian *** Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution *** detection results of the two models were fused at decision level using Dempster-Shafer evidence *** establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image *** analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed *** evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.

关键词： synthetic aperture radar(SAR)data oil spill detection subjective Bayesian Faster-region convolutional neural networks(RCNN) Dempster-Shafer evidence theory

Common Transceiver LIF⁃Lidar Based on Y⁃Type Optical Fiber for Marine oil spill detection

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Journal of Harbin Institute of Technology(New Series) 2021年第5期28卷 9-14页

作者： Zongjie Bi Songlin Yin Yanchao Zhang Zihao Cui Zhaoshuo Tian Shiyou Fu Information Optoelectronics Research Institute Harbin Institute of Technology(Weihai)Weihai 264209ShandongChina Shandong Institute of Shipbuilding Technology Weihai 264209ShandongChina

This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver for this LIF⁃Lidar *** LIF⁃Lidar system was further applied to measure the excitation spectra from floating oil slicks with different thicknesses on top of seawater at different *** system presents several advantages such as compact structure,stable optical path,and convenient operation,which offers a wide application prospect in ocean exploration.

关键词： LIF⁃Lidar Y⁃type optical fiber common transceiver oil spill detection

The polarimetric features of oil spills in full polarimetric synthetic aperture radar images

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Acta Oceanologica Sinica 2017年第5期36卷 105-114页

作者： ZHENG Honglei ZHANG Yanmin WANG Yunhua ZHANG Xi MENG Junmin College of Information Science and Engineering Ocean University of China The First Institute of Oceanography State Oceanic Administration

Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the scattering targets. Therefore, the polarimetric SAR has more advantages for oil spill detection on the sea surface. As a crucial step in the oil spill detection, a feature extraction directly influences the accuracy of oil spill discrimination. The polarimetric features of sea oil spills, such as polarimetric entropy, average scatter angle, in the full polarimetric SAR images are analyzed firstly. And a new polarimetric parameter P which reflects the proportion between Bragg and specular scattering signals is proposed. In order to investigate the capability of the polarimetric features for observing an oil spill, systematic comparisons and analyses of the multipolarization features are provided on the basis of the full polarimetric SAR images acquired by SIR-C/X-SAR and Radarsat-2. The experiment results show that in C-band SAR images the oil spills can be detected more easily than in L-band SAR images under low to moderate wind speed conditions. Moreover, it also finds that the new polarimetric parameter is sensitive to the sea surface scattering mechanisms. And the experiment results demonstrate that the new polarimetric parameter and pedestal height perform better than other polarimetric parameters for the oil spill detection in the C-band SAR images.

关键词： full polarimetric synthetic aperture radar oil spill detection mulfipolarization features

A Combination Method of Differential Evolution Algorithm and Neural Network for Automatic Identification oil spill at Vietnam East Sea

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Journal of Geological Resource and Engineering 2016年第4期4卷 184-193页

作者： Hang Le Minh Truong Vu Van Department of Geodesy and Mapping Le Quy Don Technical University Ha Noi Viet Nam

This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and BP （back propagation） algorithm. Here, DE and BP are combined to train a multilayer perceptron （MLP） network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics ofoil spills （e.g. area, perimeter, complexity） and the physical behavior ofoil spills （e,g. mean or max backscatter value, standard deviation of the dark formation）. The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.

关键词： SAR oil spill detection MLP BP algorithm DE.