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  • Open Access


    A Sea Ice Recognition Algorithm in Bohai Based on Random Forest

    Tao Li1, Di Wu1, Rui Han2, Jinyue Xia3, Yongjun Ren4,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3721-3739, 2022, DOI:10.32604/cmc.2022.029619

    Abstract As an important maritime hub, Bohai Sea Bay provides great convenience for shipping and suffers from sea ice disasters of different severity every winter, which greatly affects the socio-economic and development of the region. Therefore, this paper uses FY-4A (a weather satellite) data to study sea ice in the Bohai Sea. After processing the data for land removal and cloud detection, it combines multi-channel threshold method and adaptive threshold algorithm to realize the recognition of Bohai Sea ice under clear sky conditions. The random forests classification algorithm is introduced in sea ice identification, which can achieve a certain effect of… More >

  • Open Access


    Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net

    Runzhe Tao1, *, Yonghong Zhang1, Lihua Wang1, Pengyan Cai1, Haowen Tan2

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2455-2474, 2020, DOI:10.32604/cmc.2020.011526

    Abstract Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of… More >

  • Open Access


    Threshold-Based Adaptive Gaussian Mixture Model Integration (TA-GMMI) Algorithm for Mapping Snow Cover in Mountainous Terrain

    Yonghong Zhang1,2, Guangyi Ma1,2,*, Wei Tian3, Jiangeng Wang4, Shiwei Chen1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1149-1165, 2020, DOI:10.32604/cmes.2020.010932

    Abstract Snow cover is an important parameter in the fields of computer modeling, engineering technology and energy development. With the extensive growth of novel hardware and software compositions creating smart, cyber physical systems’ (CPS) efficient end-to-end workflows. In order to provide accurate snow detection results for the CPS’s terminal, this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model (GMM) for the FY-4A satellite data. At present, most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum, which is based on the normalized difference snow index (NDSI) with thresholds in different wavebands. These… More >

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