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Automatic Ice thickness estimation in radar imagery based on charged particles concept

机译:基于带电粒子概念的雷达图像冰层厚度自动估计

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Accelerated loss of ice from Greenland and Antarctica has been observed in recent decades. Ice thickness is a key factor in making predictions about the future of massive ice reservoirs and can be estimated by calculating the exact location of the ice surface and bottom in radar imagery. Identifying the locations of ice boundaries is typically performed manually which is a very time consuming procedure. Here we propose a novel approach which automatically detects the complex topology of ice surface and bottom boundaries based on charged particle concept. Here we first applied anisotropic diffusion to remove the noise and enhance the image. At the second step, we detected the contours in the image based on Coulomb's electrostatic law and the assumption that each pixel is an electrically charged particle. The final ice surface and bottom are detected based on the projection profile of the contours. The results are evaluated on a large dataset of airborne radar imagery collected during IceBridge mission over Antarctica and show promising results with respect to hand-labeled ground truth.
机译:近几十年来,观察到格陵兰和南极洲冰的加速流失。冰层厚度是预测大型冰库未来的关键因素,可以通过计算雷达图像中冰层表面和底部的确切位置来估算。通常,要手动确定冰边界的位置,这是非常耗时的过程。在这里,我们提出了一种新颖的方法,该方法基于带电粒子的概念自动检测冰面和底部边界的复杂拓扑。在这里,我们首先应用各向异性扩散来消除噪声并增强图像。在第二步中,我们根据库仑的静电定律和每个像素都是带电粒子的假设,检测了图像中的轮廓。根据轮廓的投影轮廓来检测最终的冰面和冰底。在对南极的IceBridge任务期间收集的大量机载雷达图像数据集上对结果进行了评估,结果在手工标记的地面真相方面显示出可喜的结果。

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