Automatic polar ice thickness estimation from SAR imagery
Automatic polar ice thickness estimation from SAR imagery
复制标题
根据 SAR 图像自动估计极地冰厚度
DOI：
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发表时间：
2016
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通讯作者：
G. Fox
中科院分区：
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作者：
M. Rahnemoonfar;M. Yari;G. Fox
近年来，全球变暖对我们的环境造成了严重破坏。近几十年来，格陵兰岛和南极洲的冰层加速流失。极地冰盖和山地冰川的融化对海平面上升和洋流变化产生了相当大的影响，可能导致沿海地区洪水泛滥，使世界各地数百万人面临危险。合成孔径雷达（SAR）系统能够提供有关极地冰盖地下结构的相关信息。手动层识别极其繁琐且昂贵，并且对于定期、长期的冰盖监测来说并不实用。由于巨大的噪声、有限的分辨率以及冰层和基岩的变化，在噪声雷达图像中自动寻找层非常具有挑战性。在这里，我们提出了一种使用距离正则化水平集演化自动检测冰面和基岩边界的方法。在这种方法中，可以通过发展雷达图像中的初始曲线来同时检测冰和基岩边界层的复杂拓扑。使用距离正则项，本质上保持了水平集函数的规律性，解决了传统水平集方法引起的重新初始化问题。结果在冰桥任务期间在南极洲和格陵兰岛收集的机载雷达图像的大型数据集上进行了评估，并在手工标记的地面实况方面显示出有希望的结果。
Global warming has caused serious damage to our environment in recent years. Accelerated loss of ice from Greenland and Antarctica has been observed in recent decades. The melting of polar ice sheets and mountain glaciers has a considerable influence on sea level rise and altering ocean currents, potentially leading to the flooding of the coastal regions and putting millions of people around the world at risk. Synthetic aperture radar (SAR) systems are able to provide relevant information about subsurface structure of polar ice sheets. Manual layer identification is prohibitively tedious and expensive and is not practical for regular, longterm ice-sheet monitoring. Automatic layer finding in noisy radar images is quite challenging due to huge amount of noise, limited resolution and variations in ice layers and bedrock. Here we propose an approach which automatically detects ice surface and bedrock boundaries using distance regularized level set evolution. In this approach the complex topology of ice and bedrock boundary layers can be detected simultaneously by evolving an initial curve in radar imagery. Using a distance regularized term, the regularity of the level set function is intrinsically maintained that solves the reinitialization issues arising from conventional level set approaches. The results are evaluated on a large dataset of airborne radar imagery collected during IceBridge mission over Antarctica and Greenland and show promising results in respect to hand-labeled ground truth.