无人机热红外遥感煤火探测方法
Approach of Detecting Coal Fires by Unmanned Aerial Vehicle Thermal Infrared Remote Sensing Technology
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摘要: 为了提高矿区煤火识别的精度，利用无人机搭载数码相机和热红外相机分别在白天和夜晚采集RGB图像和热红外图像，基于面向对象的分类方法将矿区彩色正射影像分类并赋予对应类别的发射率值；热红外影像经过辐射定标后镶嵌为正射影像，根据辐射传导方程和Plank反函数反演矿区地表温度，采用移动窗口热异常提取算法识别煤火区。试验表明，实测煤火点与无人机热红外技术探测的煤火区的重叠率为96.72%，说明无人机热红外遥感煤火探测方法的精度可靠，技术可行。Abstract: To improve the accuracy of coal fire detection in a mine, RGB and thermal infrared images were respectively collected at daytime and night time using UAV equipped with color and thermal infrared camera. Based on object oriented classification method, color orthophoto images were classified in mining area and the emission rate of corresponding categories were given. The thermal infrared image is inlaid as a positive projective image after radiation calibration, the land surface temperature is inversed according to the radiation conduction equation and the Plank function, and the thermal anomaly extraction algorithm is used to identify the coal fire zone. The test shows that the overlap ratio of the coal fire spot and the thermal infrared detection by unmanned aerial vehicle is 96.72%, which indicates that the accuracy of thermal infrared remote sensing coal fire detection method by unmanned aerial vehicle is reliable and the technology is feasible.
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