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Volume 9 Issue 3

Jul.  1992

Article Contents

On Accurate Detection of Oceanic Features from Satellite IR Data Using ICSED Method


doi: 10.1007/BF02656948

  • ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the perform-ances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.
  • [1] Myoung-Hwan AHN, Eun-Ha SOHN, Byong-Jun HWANG, 2003: A New Algorithm for Sea Fog/Stratus Detection Using GMS-5 IR Data, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 899-913.  doi: 10.1007/BF02915513
    [2] Li Jun, Zhou Fengxian, Gao Qinghuai, 1990: Delineation of Mesoscale Features of Ocean on Satellite IR Image, ADVANCES IN ATMOSPHERIC SCIENCES, 7, 423-432.  doi: 10.1007/BF03008872
    [3] Ruiyao CHEN, Ralf BENNARTZ, 2021: Rainfall Algorithms Using Oceanic Satellite Observations from MWHS-2, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1367-1378.  doi: 10.1007/s00376-020-0258-5
    [4] Shen YAN, Jie XIANG, Huadong DU, 2019: Determining Atmospheric Boundary Layer Height with the Numerical Differentiation Method Using Bending Angle Data from COSMIC, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 303-312.  doi: 10.1007/s00376-018-7308-2
    [5] Zibo ZHUANG, Kunyun LIN, Hongying ZHANG, Pak-Wai CHAN, 2024: Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record (QAR) Data Analysis, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3195-x
    [6] ZENG Heqing, JIA Gensuo, 2013: Impacts of Snow Cover on Vegetation Phenology in the Arctic from Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1421-1432.  doi: 10.1007/s00376-012-2173-x
    [7] Li Jun, Wang Luyi, Zhou Fengxian, 1993: Convective and Stratiform Cloud Rainfall Estimation from Geostationary Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 475-480.  doi: 10.1007/BF02656972
    [8] Li Jun, Zhou Fengxian, Gao Qinghuai, 1991: Satellite Data Reduction Using Entropy-preserved Image Compression Technique, ADVANCES IN ATMOSPHERIC SCIENCES, 8, 237-242.  doi: 10.1007/BF02658097
    [9] LIU Chuanxi, LIU Yi, Xiong LIU, Kelly CHANCE, 2013: Dynamical and Chemical Features of a Cutoff Low over Northeast China in July 2007: Results from Satellite Measurements and Reanalysis, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 525-540.  doi: 10.1007/s00376-012-2086-8
    [10] Jun LI, Wei HAN, 2017: A Step Forward toward Effectively Using Hyperspectral IR Sounding Information in NWP, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1263-1264.  doi: 10.1007/s00376-017-7167-2
    [11] Seung-Woo LEE, Dong-Kyou LEE, 2011: Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 758-774.  doi: 10.1007/s00376-010-0145-6
    [12] Shengzhe CHEN, Jiping LIU, Yifan DING, Yuanyuan ZHANG, Xiao CHENG, Yongyun HU, 2021: Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 168-186.  doi: 10.1007/s00376-020-0213-5
    [13] Yanni Qu, Mitchell D. Goldberg, Murty Divakarla, 2001: Ozone Profile Retrieval from Satellite Observation Using High Spectral Resolution Infrared Sounding Instrument, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 959-971.
    [14] Yang Yang, Minqiang Zhou, Wei Wang, Zijun Ning, Feng Zhang, Pucai Wang, 2024: Quantification of CO2 emissions from three power plants in China using OCO-3 satellite measurements, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3293-9
    [15] Xi WANG, Zheng GUO, Yipeng HUANG, Hongjie FAN, Wanbiao LI, 2017: A Cloud Detection Scheme for the Chinese Carbon Dioxide Observation Satellite (TANSAT), ADVANCES IN ATMOSPHERIC SCIENCES, 34, 16-25.  doi: 10.1007/s00376-016-6033-y
    [16] Jinqiang ZHANG, Hongbin CHEN, Xiang'ao XIA, Wei-Chyung WANG, 2016: Dynamic and Thermodynamic Features of Low and Middle Clouds Derived from Atmospheric Radiation Measurement Program Mobile Facility Radiosonde Data at Shouxian, China, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 21-33.  doi: 10.1007/s00376-015-5032-8
    [17] XUE Hai-Le, SHEN Xue-Shun, CHOU Ji-Fan, 2013: A Forecast Error Correction Method in Numerical Weather Prediction by Using Recent Multiple-time Evolution Data, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1249-1259.  doi: 10.1007/s00376-013-2274-1
    [18] ZHANG Xin, LIU Yuewei, WANG Bin, JI Zhongzhen, 2004: Parallel Computing of a Variational Data Assimilation Model for GPS/MET Observation Using the Ray-Tracing Method, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 220-226.  doi: 10.1007/BF02915708
    [19] Chuan GAO, Xinrong WU, Rong-Hua ZHANG, 2016: Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 875-888.  doi: 10.1007/s00376-016-5249-1
    [20] XU Dongmei, Thomas AULIGNÈ, Xiang-Yu HUANG, 2015: A Validation of the Multivariate and Minimum Residual Method for Cloud Retrieval Using Radiance from Multiple Satellites, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 349-362.  doi: 10.1007/s00376-014-3258-5

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Manuscript History

Manuscript received: 10 July 1992
Manuscript revised: 10 July 1992
通讯作者: 陈斌, bchen63@163.com
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On Accurate Detection of Oceanic Features from Satellite IR Data Using ICSED Method

  • 1. Institute of Atmospheric Physics, Academia Sinica, Beijing 100029,Institute of Atmospheric Physics, Academia Sinica, Beijing 100029

Abstract: ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the perform-ances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.

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