无人机热红外遥感煤火探测方法
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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。
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.
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.
引文
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[11]Kuenzer C,Zhang J,Li J,et al.Detecting unknown coal fires:synergy of automated coal fire risk area delineation and improved thermal anomaly extraction[J].International Journal of Remote Sensing,2007,28(20):4561-4585.
[2]李峰,梁汉东,赵小平,等.基于ASTER影像的乌达火区遥感监测研究[J].煤矿安全,2016,47(11):15-18.
[3]Jiang W,Jia K,Chen Z,et al.Using spatiotemporal remote sensing data to assess the status and effectiveness of the underground coal fire suppression efforts during2000-2015 in Wuda,China[J].Journal of Cleaner Production,2017,142:565-577.
[4]Huo H,Ni Z,Gao C,et al.A study of coal fire propagation with remotely sensed thermal infrared data[J].Remote Sensing,2015,7(3):3088-3113.
[5]Zhang X,Zhang J,Kuenzer C,et al.Capability evaluation of 3-5μm and 8-12.5μm airborne thermal data for underground coal fire detection[J].International Journal of Remote Sensing,2004,25(12):2245-2258.
[6]Wang Y J,Tian F,Huang Y,et al.Monitoring coal fires in Datong coalfield using multi-source remote sensing data[J].Transactions of Nonferrous Metals Society of China,2015,25(10):3421-3428.
[7]Turner D,Lucieer A,Malenovsky Z,et al.Spatial coregistration of ultra-high resolution visible,multispectral and thermal images acquired with a micro-UAV over Antarctic moss beds[J].Remote Sensing,2014(6):4003.
[8]Harvey M C,Rowland J V,Luketina K M.Drone with thermal infrared camera provides high resolution georeferenced imagery of the Waikite geothermal area,New Zealand[J].Journal of Volcanology&Geothermal Research,2016,325:61-69.
[9]Santesteban L G,Gennaro S F D,Herrero-Langreo A,et al.High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard[J].Agricultural Water Management,2016,183:49-59.
[10]易凤佳,李仁东,常变蓉,等.面向对象的丘陵区水田遥感识别方法[J].农业工程学报,2015,31(11):186-193.
[11]Kuenzer C,Zhang J,Li J,et al.Detecting unknown coal fires:synergy of automated coal fire risk area delineation and improved thermal anomaly extraction[J].International Journal of Remote Sensing,2007,28(20):4561-4585.