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Using high-resolution UAV-borne thermal infrared imagery to detect coal fires in Majiliang mine, Datong coalfield, Northern China

机译:利用高分辨率的无人机热红外图像检测中国北方大同煤田马吉梁矿的煤火

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摘要

Underground coal fires occur normally under inaccessible dangerous steep hills. As a light-weight and cost-effective equipment, unmanned aerial vehicles (UAVs)-based thermal infrared (TIR) imaging technique provides a choice to safely, timely and accurately map characteristics of coal fires which is difficult to realize using the conventional technologies. The colour images captured by UAV are used to generate a map of land cover types and estimate emissivity of ground features. TIR images are adopted to retrieve land surface temperature (LST) and thus generate orthophotos, which will be further used to recognize coal fire areas. The retrieved LST at night is validated with reference LST, and the results show yielding Root Mean Square Error (RMSE) is less than 1.03 K. The accuracy rate of identified coal fire areas at night time reaches as high as 92.78%, which is higher than that of daytime on 2nd October, 2016 and 5th October, 2016. In this research, the application of UAV-borne thermal imaging demonstrates a great potential to precisely and rapidly describe features of coal fires.
机译:地下煤火通常发生在难以接近的危险陡峭山丘下。作为一种轻巧且具有成本效益的设备,基于无人机(UAV)的热红外(TIR)成像技术为安全,及时和准确地绘制煤火特征提供了一种选择,这是使用传统技术难以实现的。 UAV捕获的彩色图像用于生成土地覆盖类型图并估计地面要素的发射率。采用TIR图像检索地表温度(LST),从而生成正射影像,并将其进一步用于识别煤火区域。使用参考LST对夜间检索到的LST进行了验证,结果表明,产生的均方根误差(RMSE)小于1.03K。夜间识别出的煤火区域的准确率高达92.78%,较高与2016年10月2日和2016年10月5日的白天相比,这项研究显示,无人机载热成像技术的应用展示了精确快速描述煤火特征的巨大潜力。

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  • 来源
    《Remote sensing letters》 |2018年第3期|71-80|共10页
  • 作者单位

    Inst Disaster Prevent, Dept Disaster Prevent Engn, Sanhe, Peoples R China;

    Inst Disaster Prevent, Dept Disaster Prevent Engn, Sanhe, Peoples R China;

    Inst Disaster Prevent, Dept Disaster Prevent Engn, Sanhe, Peoples R China;

    Inst Disaster Prevent, Dept Disaster Prevent Engn, Sanhe, Peoples R China;

    Inst Disaster Prevent, Dept Disaster Prevent Engn, Sanhe, Peoples R China;

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