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

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

doi:10.1007/s13131-014-0502-x

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Article Navigation > Acta Oceanologica Sinica > 2014 > 33(7): 65-71

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

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

Received Date: 2013-04-26
Rev Recd Date: 2013-11-11

Abstract

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.
- sea surface temperature,
- threshold setting,
- Sobel algorithm,
- edge detection

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References(17)

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