基于高分辨率卫星影像的输电走廊植被生长预警
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TM755

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国网四川省电力有限公司科技项目(521997170013)


Forewarning of vegetation growth in transmission corridor based on high resolution satellite images
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    摘要:

    为从卫星影像中提取出输电走廊的信息,提出一种基于高分二号遥感影像的输电走廊信息提取方法。首先基于低分辨率多光谱影像进行加权灰度变换并实现影像二值化,利用影像边缘检测和直线检测方法提取输电线路和杆塔的信息。然后,利用最小距离法对融合后的高分辨率影像进行植被精细识别,并计算植被覆盖指数。最后, 在植被分布影像中重构输电线路,并结合植被覆盖指数对植被威胁区域进行预警。仿真结果表明:利用边缘检测对二值化影像进行杆塔提取识别率达到100%,融合后的植被分布影像轮廓清晰,对地物植被覆盖区域识别精度达到了90%以上,结合植被覆盖指数给出的预警区存在植被生长茂密,威胁输电线路运行的情况。该方法实现输电走廊植被生长预警,能够运用于电力行业的输电安全在线监测。

    Abstract:

    In order to extract the information of transmission corridor from satellite images, a method based on the highresolution remote sensing image No. 2 is proposed. Firstly, the weighted grayscale transformation based on lowresolution multispectral images is applied to obtain binary images. The information of transmission lines and towers is extracted by image edge detection and line detection methods. Then, the fused highresolution images are identified by the minimum distance method, and the vegetation coverage index is calculated. Finally, the transmission line is reconstructed in the vegetation distribution image, and the vegetation coverage index is utilized to warn the vegetation threatening areas. The simulation results show that the recognition rate of pole and tower extraction from binary image by edge detection is 100%, the contour of the fused vegetation distribution image is clear, and the recognition accuracy of vegetation coverage area is over 90%. Combined with the vegetation coverage index, the early warning area has dense vegetation growth, which threatens the safe operation of transmission lines. The proposed method realizes the early warning of vegetation growth in the transmission corridor, and can be applied in the transmission safety online monitoring of power industry.

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刘凤莲,曹永兴,高润明,等.基于高分辨率卫星影像的输电走廊植被生长预警[J].电力科学与技术学报,2021,36(3):188-194.
Liu Fenglian, Cao Yongxing, Gao Runming, et al. Forewarning of vegetation growth in transmission corridor based on high resolution satellite images[J]. Journal of Electric Power Science and Technology,2021,36(3):188-194.

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  • 在线发布日期: 2021-08-26
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