Multi-satellite Monitoring SST Data Fusion based on the Adaptive Threshold Clustering Algorithm

被引:2
|
作者
Shi, Hongmei [1 ]
Dong, Han [2 ]
Xu, Lingyu [1 ]
Song, Cuicui [1 ]
Zhong, Fei [1 ]
Su, Ruidan [3 ]
机构
[1] Shanghai Univ, Dept Comp Engn & Sci, Shanghai 200072, Peoples R China
[2] Natl Marine Informat Ctr, Tianjin 300171, Peoples R China
[3] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
information fusion; sea surface temperature (SST); variable precision; threshold; clustering; adaptive threshold;
D O I
10.4304/jcp.7.10.2593-2598
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper proposes a method which describes the information precision with a soft fusion model, instead of the traditional rigid fusion method. The method is divided into two steps, the pretreatment model and fusion center model. Each forms a relative independent model, and the two models have a progressive relationship. The former is used for consistency evaluation, data cleaning and invalid data eliminating, while the latter provides fusion results and variable precision fusion expression by the adaptive threshold clustering algorithm. Experimental results show that the fusion method can not only give every SST data a different precision, but also carry more information to describe precision multiple distribution, which make users get high-quality data and enjoy more rights.
引用
收藏
页码:2593 / 2598
页数:6
相关论文
共 50 条
  • [1] Research of Multi-satellite Tracking algorithm Based on Data Fusion
    Chen Hongying
    Guo Caifa
    Wang Xuliang
    Li Huifen
    2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2015, : 169 - 172
  • [2] RETRACTED: The clustering analysis of multi-satellite monitoring SST data on the basis of variable precision (Retracted Article)
    Wang, Jian
    Xu, Lingyu
    Zhong, Fei
    Xu, Yijun
    Liu, Na
    2011 INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENTAL SCIENCE-ICEES 2011, 2011, 11 : 1163 - 1171
  • [3] AGRICULTURAL MONITORING USING MULTI-SATELLITE DATA
    Ichikawa, D.
    Wakamori, K.
    Oguri, N.
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 5077 - 5080
  • [4] Advance in Monitoring Forest Fire in China Based on Multi-Satellite Data
    Zhang, Jiahua
    Yao, Fengmei
    ADVANCES IN ENVIRONMENTAL SCIENCE AND ENGINEERING, PTS 1-6, 2012, 518-523 : 5668 - 5672
  • [5] Multi-satellite observation scheduling based on task clustering
    Wu, Guohua
    Ma, Manhao
    Wang, Huilin
    Qiu, Dishan
    Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 2011, 32 (07): : 1275 - 1282
  • [6] New SST correction method from multi-satellite based on the coefficient of variation
    钟飞
    刘娜
    刘洋
    徐凌宇
    Advances in Manufacturing, 2011, (05) : 463 - 466
  • [7] An improved adaptive genetic algorithm for multi-satellite area observation scheduling
    Fan Yu
    Liu Yingying
    Zhou Jun
    CHINESE SPACE SCIENCE AND TECHNOLOGY, 2021, 41 (01) : 38 - 47
  • [8] Data-driven based network predictive scheduling algorithm for multi-satellite tasks
    Cheng X.-J.
    Cui K.-X.
    Zhang L.
    Liu W.
    Shi D.-W.
    Kongzhi yu Juece/Control and Decision, 2024, 39 (03): : 749 - 758
  • [9] Fusion of Multi-Satellite Data and Artificial Neural Network for Predicting Total Discharge
    Seo, Jae Young
    Lee, Sang-Il
    REMOTE SENSING, 2020, 12 (14)
  • [10] Rolling estimation model of soil moisture based on multi-satellite fusion
    Liang Y.
    Ren C.
    Huang Y.
    Wang H.
    Lu X.
    Yan H.
    Yaogan Xuebao/Journal of Remote Sensing, 2019, 23 (04): : 648 - 660