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A model of sea surface temperature front detection based on a threshold interval

PING Bo,  SU Fenzhen,  MENG Yunshan,  FANG Shenghui,  DU Yunyan

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文章导航 > Acta Oceanologica Sinica  > 2014 >  33(7): 65-71
PINGBo, SUFenzhen, MENGYunshan, FANGShenghui, DUYunyan. 基于阈值区间的海洋锋面提取模型[J]. 海洋学报英文版, 2014, 33(7): 65-71. doi: 10.1007/s13131-014-0502-x
引用本文: PINGBo, SUFenzhen, MENGYunshan, FANGShenghui, DUYunyan. 基于阈值区间的海洋锋面提取模型[J]. 海洋学报英文版, 2014, 33(7): 65-71. doi: 10.1007/s13131-014-0502-x
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PING Bo, SU Fenzhen, MENG Yunshan, FANG Shenghui, DU Yunyan. A model of sea surface temperature front detection based on a threshold interval[J]. Acta Oceanologica Sinica, 2014, 33(7): 65-71. doi: 10.1007/s13131-014-0502-x
Citation: PING Bo, SU Fenzhen, MENG Yunshan, FANG Shenghui, DU Yunyan. A model of sea surface temperature front detection based on a threshold interval[J]. Acta Oceanologica Sinica, 2014, 33(7): 65-71. doi: 10.1007/s13131-014-0502-x
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基于阈值区间的海洋锋面提取模型

doi: 10.1007/s13131-014-0502-x
  • 1.

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Collaborative Innovation Center of South China Sea Studies, Nanjing 210093, China

  • 2.

    Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Collaborative Innovation Center of South China Sea Studies, Nanjing 210093, China

  • 3.

    Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • 4.

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

基金项目: The National Key Technology R&D Program of China under contract No. 2011BAH23B04;the National High Technology Research and Development Program (863 Program) of China under contract No. 2007AA092202.

A model of sea surface temperature front detection based on a threshold interval

  • 1.

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Collaborative Innovation Center of South China Sea Studies, Nanjing 210093, China

  • 2.

    Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Collaborative Innovation Center of South China Sea Studies, Nanjing 210093, China

  • 3.

    Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • 4.

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

    摘要
  • 摘要: 本文提出BOFD模型（Bayesian Oceanic Front Detection），该模型基于阈值区间，对海洋温度锋面进行检测。模型首先利用Sobel算子计算海表温度梯度，根据梯度运算结果，利用梯度累积直方图，分别选择高低阈值，设定梯度值在高低阈值之间的像素点为待定点。根据阈值区间，计算待定点的先验概率。引入像素点的边缘局部度和局部偏差度，在上述的阈值范围内，计算各待定点在属于锋面和非锋面情况下的似然函数。最后基于贝叶斯决策论判定像素点是否属于海洋锋面点。基于本文模型，对黑潮区域的锋面进行检测，证实本模型可以在检测海洋锋面的同时，减少噪声的干扰，提高海洋锋面提取的精度。
    Abstract: A model (Bayesian oceanic front detection, BOFD) of sea surface temperature (SST) front detection in satellite-derived SST images based on a threshold interval is presented, to be used in different applications such as climatic and environmental studies or fisheries. The model first computes the SST gradient by using a Sobel algorithm template. On the basis of the gradient value, the threshold interval is determined by a gradient cumulative histogram. According to this threshold interval, front candidates can be acquired and prior probability and likelihood can be calculated. Whether or not the candidates are front points can be determined by using the Bayesian decision theory. The model is evaluated on the Advanced Very High-Resolution Radiometer images of part of the Kuroshio front region. Results are compared with those obtained by using several SST front detection methods proposed in the literature. This comparison shows that the BOFD not only suppresses noise and small-scale fronts, but also retains continuous fronts.
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    • 参考文献(17)
  • Belkin I, Cornillon P, Sherman K. 2009. Fronts in large marine ecosystems. Progress in Oceanography, 81: 223-236
    Canny J. 1986. A computational approach to edge detection. IEEE Transactions Pattern Analysis and Machine Intelligence PAMI, 8(6): 679-698
    Cayula J F, Cornillon P. 1992. Edge detection algorithm for SST images. Journal of Atmospheric and Oceanic Technology, 9: 67-80
    Cayula J F, Cornillon P. 1995. Multi-image edge detection for SST images. Journal of Atmospheric and Oceanic Technology, 12: 821-829
    Diehl F, Budd W, Ullman D, et al. 2001. Geographic window sizes applied to remote sensing sea surface temperature front detection. Journal of Atmospheric and Oceanic Technology, 19: 1105-1113
    Duda R, Hart P, Stock D. 2003. Pattern Classification (in Chinese). Beijing: China Ocean Press, 16-18
    Huh O K. 1982. Satellite observations and the annual cycle of surface circulation in the Yellow Sea, East China Sea, and Korea Strait. La Mer, 20: 210-222
    Janowitz M F. 1985. Automatic Detection of Gulf Stream Rings. MASSACHUSETTS UNIV AMHERST, No. TR-J850
    Mansoori E, Eghbali H. 2006. Heuristic edge detection using fuzzy rule-based classifier. Journal of Intelligent and Fuzzy Systems, 17: 457-469
    Oram J, Mcwilliams J, Stolzenbach K. 2008. Gradient-based edge detection and feature classification of sea-surface images of the southern California Bight. Remote Sensing of Environment, 112(5): 2397-2415
    Rivas A, Pisoni J. 2009. Identification, characteristics and seasonal evolution of surface thermal fronts in the Argentinean continental shelf. Journal of Marine Systems, 79: 134-143
    Sauter D, Parson L. 1994. Spatial filtering for speckle reduction contrast enhancement, and texture analysis of GLORIA images. IEEE J Oceanic Eng, 19: 563-576
    Savage C R, Petrell R J, Neufeld T P. 1994. Underwater fish video images: image quality and edge detection techniques. Can Agric End, 36: 175-183
    Shimada T, Sakaida F, Kawamura H, et al. 2005. Application of an edge detection method to satellite images for distinguishing sea surface temperature fronts near the Japanese coast. Remote Sensing of Environment, 98(1): 21-34
    Shi Hanqing, Zhang Wei, Yin Zhiquan. 2010. Study on ocean front detection based on Canny operator and mathematical morphology. International Conference on Remote Sensing (in Chinese). China: China Academic Journal Electronic Publishing House, 4: 252-255
    Vázquez D P, Chakir Atae-Allah, Pedro L L.1999. Entropic approach to edge detection for SST images. Journal of Atmospheric and Oceanic Technology, 16: 970-979
    Zhang Hao, Cai Jinhui, Huang Pingjie, et al. 2007. Edge detection of complex scenes based on Bayesian statistical inference. Journal of South China University of Technology: Natural Science Edition (in Chinese), 35(9): 40-44
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出版历程
  • 收稿日期:  2013-04-26
  • 修回日期:  2013-11-11

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