Spacecraft Recovery & Remote Sensing, Volume. 45, Issue 5, 147(2024)

Real-Time Fire Detection by Cascading Traditional Approaches with Deep Learning

Wenzhuo WANG1, Chenglong MA2, Guanlin WANG1, Yiming ZHANG1, Fangxiong TAN2、*, Xu HAN3, and Lei WU3
Author Affiliations
  • 1State Grid Gansu Electric Power Company, Lanzhou 730000, China
  • 2State Grid Gansu Electric Power Company Jiuquan Power Supply Company, Jiuquan 735000, China
  • 3Beijing Deep Blue Space Remote Sensing Technology Co., Ltd., Beijing 100020, China
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    Figures & Tables(9)
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    • Table 1. Himawari-8 band introduction

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      Table 1. Himawari-8 band introduction

      类别波段序号中心波长/μm空间分辨率/km
      可见光R10.471.0
      R20.511.0
      R30.640.5
      近红外R40.861.0
      R51.62.0
      R62.32.0
      红外T73.92.0
      T86.22.0
      T96.92.0
      T107.32.0
      T118.62.0
      T129.62.0
      T1310.42.0
      T1411.22.0
      T1512.42.0
      T1613.32.0
    • Table 2. Model input features

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      Table 2. Model input features

      分组类型输入特征
      1原始光谱信息R1R2R3R4R5R6T7T8
      T9T10T11T12T13T14T15T16
      2亮温差值T7-T8T7-T9T7-T10T7-T11T7-T12T7-T13T7-T14T7-T15T7-T16
      T8-T9T8-T10T8-T11T8-T12T8-T13T8-T14T8-T15T8-T16
      T9-T10T9-T11T9-T12T9-T13T9-T14T9-T15T9-T16
      T10-T11T10-T12T10-T13T10-T14T10-T15T10-T16
      T11-T12T11-T13T11-T14T11-T15T11-T16
      T12-T13T12-T14T12-T15T12-T16
      T13-T14T13-T15T13-T16
      T14-T15T14-T16
      T15-T16
      亮温比值T7/T8T7/T9T7/T10T7/T11T7/T12T7/T13T7/T14T7/T15T7/T16
      T8/T9T8/T10T8/T11T8/T12T8/T13T8/T14T8/T15T8/T16
      T9/T10T9/T11T9/T12T9/T13T9/T14T9/T15T9/T16
      T10/T11T10/T12T10/T13T10/T14T10/T15T10/T16
      T11/T12T11/T13T11/T14T11/T15T11/T16
      T12/T13T12/T14T12/T15T12/T16
      T13/T14T13/T15T13/T16
      T14/T15T14/T16
      T15/T16
      3空间上下文信息MEAN_T7MEAN_T14MEAN_BT7
      STD_T7STD_T14STD_BT7
      4地理差异DEMSlopeAspectLanduseNDVILonLat
      时间差异DOYHour_minute
      观测角度SAZSAAS0ZSOA
    • Table 3. Confusion matrix

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      Table 3. Confusion matrix

      类别预测为火点预测为非火点
      真实火点TPFN
      真实非火点FPTN
    • Table 4. Model prediction results

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      Table 4. Model prediction results

      模型$ \mathit{P} $$ \mathit{M} $$ \mathit{F} $
      Model1-CNN0.720.270.73
      Model2-CNN0.770.180.79
      Model3-CNN0.830.140.84
      PMCNN0.810.140.83
      MCNN0.880.110.88
    • Table 5. Comparison of fire point recognition accuracy

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      Table 5. Comparison of fire point recognition accuracy

      类别识别火点总数/个正确数/个漏报数/个$ P $$ M $$ F $
      MCNN10388180.850.170.84
      WLF82240.23
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    Wenzhuo WANG, Chenglong MA, Guanlin WANG, Yiming ZHANG, Fangxiong TAN, Xu HAN, Lei WU. Real-Time Fire Detection by Cascading Traditional Approaches with Deep Learning[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(5): 147

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    Paper Information

    Category:

    Received: Nov. 24, 2023

    Accepted: --

    Published Online: Nov. 13, 2024

    The Author Email: TAN Fangxiong (2449702060@qq.com)

    DOI:10.3969/j.issn.1009-8518.2024.05.014

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