Lead Detection in Polar Oceans - A Comparison of Different Classification Methods for Cryosat-2 SAR Data
Lead Detection in Polar Oceans - A Comparison of Different Classification Methods for Cryosat-2 SAR Data
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DOI：
10.3390/rs10081190
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发表时间：
2018-07
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影响因子：
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通讯作者：
D. Dettmering;A. Wynne;F. Müller;M. Passaro;F. Seitz
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文献类型：
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作者：
D. Dettmering;A. Wynne;F. Müller;M. Passaro;F. Seitz
在极地地区，海冰阻碍了卫星测高对海面高度（SSH）的精确观测。为了获得冰内开口的可靠高度，必须完成两个步骤：(1)正确识别水（例如，铅或波利尼亚斯），这一过程称为铅分类；(2)从雷达回波中提取距离的专用重跟踪算法。本研究的重点是第一点，旨在确定Cryosat-2 SAR数据的最佳可用铅分类方法。对四种不同的高度计导联分类方法进行了比较和评估，以获得非常高分辨率的航空图像。这些方法是最大功率分类器；主要基于脉冲峰值的多参数分类方法；叠峰度的多观测值分析以及一种无监督分类方法。具有25个聚类的无监督分类方法始终表现最好，总体准确率为97%。此外，该方法不需要了解研究区域内的特定冰特征，因此是极地海洋Cryosat-2 SAR中推荐的铅检测算法。
In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans.