Oil Spill Detection Using Hyperspectral Infrared Camera

被引:0
|
作者
Yu Hui [1 ]
Wang Qun [1 ]
Zhang Zhen [2 ]
Zhang Zhi-jie [1 ]
Tang wei [2 ]
Tang Xin [2 ]
Yue Song [1 ]
Wang Chen-sheng [1 ]
机构
[1] Huazhong Inst Electropt, Wuhan Natl Lab Optoelect, 717 Yangguang Rd, Wuhan 430074, Peoples R China
[2] State Ocean Adm, North China Sea Marine Tech Support Ctr, 22 Fushun Rd, Qingdao, Peoples R China
关键词
oil spill; hyperspectral; image processing; classification; feature extraction;
D O I
10.1117/12.2244924
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Oil spill pollution is a severe environmental problem that persists in the marine environment and in inland water systems around the world. Remote sensing is an important part of oil spill response. The hyperspectral images can not only provide the space information but also the spectral information. Pixels of interests generally incorporate information from disparate component that requires quantitative decomposition of these pixels to extract desired information. Oil spill detection can be implemented by applying hyperspectral camera which can collect the hyperspectral data of the oil. By extracting desired spectral signature from hundreds of band information, one can detect and identify oil spill area in vast geographical regions. There are now numerous hyperspectral image processing algorithms developed for target detection. In this paper, we investigate several most widely used target detection algorithm for the identification of surface oil spills in ocean environment. In the experiments, we applied a hyperspectral camera to collect the real life oil spill. The experimental results shows the feasibility of oil spill detection using hyperspectral imaging and the performance of hyperspectral image processing algorithms were also validated.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Oil Spill Detection Using Machine Learning and Infrared Images
    De Kerf, Thomas
    Gladines, Jona
    Sels, Seppe
    Vanlanduit, Steve
    REMOTE SENSING, 2020, 12 (24) : 1 - 13
  • [2] Spectral Unmixing of Hyperspectral Data for Oil Spill Detection
    Sidike, P.
    Khan, J.
    Alam, M.
    Bhuiyan, S.
    OPTICS AND PHOTONICS FOR INFORMATION PROCESSING VI, 2012, 8498
  • [3] Trends in Oil Spill Detection via Hyperspectral Imaging
    Alam, Mohammad S.
    Sidike, Paheding
    2012 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (ICECE), 2012,
  • [4] Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing
    Yang, Junfang
    Hu, Yabin
    Zhang, Jie
    Ma, Yi
    Li, Zhongwei
    Jiang, Zongchen
    FRONTIERS IN MARINE SCIENCE, 2023, 10
  • [5] Oil Spill Classification Using an Autoencoder and Hyperspectral Technology
    Carrasco-Garcia, Maria Gema
    Rodriguez-Garcia, Maria Inmaculada
    Ruiz-Aguilar, Juan Jesus
    Deka, Lipika
    Elizondo, David
    Turias Dominguez, Ignacio Jose
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (03)
  • [6] Oil Spill Mapping Using Hyperspectral Methods and Techniques
    Sykas, Dimitris
    Karathanassi, Vassilia
    Andreou, Charoula
    Kookoussis, Polychronis
    MEDCOAST 11, VOLS 1 AND 2, 2011, : 651 - 662
  • [7] Hyperspectral image analysis for oil spill detection: a comparative study
    El-Rahman, Sahar A.
    Zolait, Ali Hussein Saleh
    INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2018, 9 (02) : 103 - 121
  • [8] Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging
    Zhan, Ce
    Bai, Kai
    Tu, Binrui
    Zhang, Wanxing
    SENSORS, 2024, 24 (02)
  • [9] ON-LINE DETECTION OF OIL ON STEEL COILS AND THICKNESS MEASUREMENT USING HYPERSPECTRAL CAMERA
    Ferte, M.
    Roquelet, C.
    Glijer, D.
    Carteret, C.
    Fricout, G.
    2014 6TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2014,
  • [10] Runway Detection using Pushbroom Hyperspectral Camera
    Akhter, Muhammad Awais
    Mumtaz, Adeel
    Nawaz, Rab
    PROCEEDINGS OF 2019 16TH INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES AND TECHNOLOGY (IBCAST), 2019, : 408 - 411