首页> 外文会议>International Symposium on Remote Sensing of Environment >VEGETATION HEIGHT ESTIMATION NEAR POWER TRANSMISSION POLES VIA SATELLITE STEREO IMAGES USING 3D DEPTH ESTIMATION ALGORITHMS
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VEGETATION HEIGHT ESTIMATION NEAR POWER TRANSMISSION POLES VIA SATELLITE STEREO IMAGES USING 3D DEPTH ESTIMATION ALGORITHMS

机译:植被高度估计通过使用3D深度估计算法的卫星立体图像靠近电力传输极点

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Monitoring vegetation encroachment under overhead high voltage power line is a challenging problem for electricity distribution companies. Absence of proper monitoring could result in damage to the power lines and consequently cause blackout. This will affect electric power supply to industries, businesses, and daily life. Therefore, to avoid the blackouts, it is mandatory to monitor the vegetation/trees near power transmission lines. Unfortunately, the existing approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation/trees near or under the power transmission poles using satellite stereo images, which were acquired using Pleiades satellites. The 3D depth of vegetation has been measured near power transmission lines using stereo algorithms. The area of interest scanned by Pleiades satellite sensors is 100 square kilometer. Our dataset covers power transmission poles in a state called Sabah in East Malaysia, encompassing a total of 52 poles in the area of 100 km. We have compared the results of Pleiades satellite stereo images using dynamic programming and Graph-Cut algorithms, consequently comparing satellites' imaging sensors and Depth-estimation Algorithms. Our results show that Graph-Cut Algorithm performs better than dynamic programming (DP) in terms of accuracy and speed.
机译:在顶上的高压电力线下监测植被侵占是电力分配公司的一个具有挑战性的问题。没有适当的监测可能导致电力线损坏,从而导致停电。这将影响到行业,企业和日常生活的电力供应。因此,为了避免停电,必须监视电力传输线附近的植被/树木。不幸的是,现有方法耗时且昂贵。在本文中,我们提出了一种新颖的方法来利用卫星立体图像监测近在电力传输杆附近或下方的植被/树木。使用立体声算法在电力传输线附近测量了3D植被深度。 Pleiades卫星传感器扫描的兴趣领域是100平方公里。我们的数据集涵盖了在马来西亚东部叫做沙巴的国家的电力传输极点,包括在100公里的地区共有52个杆。我们使用动态编程和图形切割算法比较了Pleiades卫星立体图像的结果,从而比较了卫星的成像传感器和深度估计算法。我们的结果表明,在精度和速度方面,图形切割算法比动态编程(DP)更好。

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