首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Dot and Segment Feature Analysis and Parameter Inversion of a Curved and Graded Bay Bridge From UAVSAR Imagery
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Dot and Segment Feature Analysis and Parameter Inversion of a Curved and Graded Bay Bridge From UAVSAR Imagery

机译:基于UAVSAR图像的弯坡海湾大桥点线特征分析与参数反演。

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When NASA/JPL UAVSAR imaged the curved and graded Coronado Bridge over San Diego Bay, CA, USA, it was shown as three separate features consisting of dots, short curved segments, and a continuously curved segment on the L-band images. Coupled with the images acquired in the opposite look directions toward the bridge, we analyzed the features, attributed the cause for the unidentifiable direct surface backscatter from the bridge roadway, and assessed the azimuth angle effects linked to the features. A two-step procedure was then articulated to resolve the azimuth angle effect on the decomposition of the quad-pol SAR imagery where orientated man-made objects and trees existed. Next, an algorithm using the single polarization data to estimate the curved and graded bridge height and width was developed. With reference to the Lidar-derived heights and the constant bridge width, we evaluated the algorithm using the difference values of 26 samples. L-HH and L-VV results of two UAVSAR datasets were obtained. Of the bridge height or width, the overall mean and one standard deviation values of the differences were <= 3.7 m and <= 2.8 m, respectively. The values were smaller than the azimuth and ground-range resolutions (5 m x 5 m) of the SAR datasets. Thus, the estimated bridge height and width should be acceptable. Finally, we studied the applicability of the algorithm to ALOS/PALSAR datasets of different data acquisition modes.
机译:当NASA / JPL UAVSAR对美国加利福尼亚州圣地亚哥湾上的弯曲且渐变的科罗纳多大桥进行成像时,它被显示为三个独立的特征,包括点,短弯曲段和L波段图像上的连续弯曲段。结合在朝向桥梁的相反方向上获取的图像,我们分析了特征,归因于桥梁巷道无法识别的直接表面反向散射的原因,并评估了与特征相关的方位角效应。然后阐明了一个两步过程来解决方位角对四向极化SAR图像分解的影响,在该图像中有定向的人造物体和树木。接下来,开发了一种使用单极化数据来估计弯曲和渐变桥高和宽的算法。参考从激光雷达得出的高度和恒定的桥宽度,我们使用26个样本的差值评估了算法。获得了两个UAVSAR数据集的L-HH和L-VV结果。在桥梁的高度或宽度中,差异的总平均值和一个标准偏差值分别<= 3.7 m和<= 2.8 m。该值小于SAR数据集的方位角分辨率和地面分辨率(5 m x 5 m)。因此,估计的桥梁高度和宽度应该可以接受。最后,我们研究了该算法对不同数据采集模式的ALOS / PALSAR数据集的适用性。

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