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:利用3D深度估计算法通过卫星立体图像对功率传输极附近的植被高度估计

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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:监视架空高压电力线下的植被入侵对于配电公司来说是一个具有挑战性的问题。缺少适当的监控可能会导致电源线损坏,从而导致停电。这将影响对工业,企业和日常生活的电力供应。因此,为避免停电,必须监视输电线路附近的植被/树木。不幸的是,现有方法更加耗时且昂贵。在本文中,我们提出了一种新颖的方法,该方法使用卫星立体图像监测输电杆附近或下方的植被/树木,该图像是使用P宿星卫星获取的。植被的3D深度已使用立体声算法在输电线路附近进行了测量。 le宿星卫星传感器扫描的关注区域为100平方公里。我们的数据集涵盖了东马来西亚沙巴州的输电杆,涵盖了100公里范围内的总共52杆。我们使用动态编程和Graph-Cut算法比较了le星卫星立体图像的结果,因此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,Graph-Cut算法在准确性和速度方面都比动态编程(DP)更好。

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