A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data
A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data
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DOI：
10.1109/igarss.2014.6946385
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Jiashu Zhang;J. Kerekes;B. Csathó;T. Schenk;R. Wheelwright
中科院分区：
文献类型：
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作者：
Jiashu Zhang;J. Kerekes;B. Csathó;T. Schenk;R. Wheelwright
卫星激光雷达仪器的观测提供了全球范围内极地冰盖显著变化的证据。冰、云和陆地高程卫星-2（ICESat-2）计划于2017年由美国宇航局发射，将监测极地冰盖和植被冠层的高程变化。为了验证ICESat-2的光子计数激光测高方法，从多高度计光束实验激光雷达（美宝）仪器获得的测量结果至关重要。为了支持ICESat-2的使命，本文推导了一种光子计数激光高度计数据中地面和植被冠层的检测算法。该方法使用基于密度的聚类模型，并修改搜索区域的形状。基于美宝观测的结果，所提出的方法被认为是强大的检测地面和植被冠层以及背景噪声的减少。此外，该方法可以快速实现，并适应于光子计数激光雷达数据集与不同的点云密度。
Observations from satellite lidar instruments have provided evidence in the remarkable changes in polar ice sheets on a global scale. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled for launch by NASA in 2017 and will monitor the elevation changes of polar ice sheets and vegetation canopy. To validate ICESat-2's approach of photon-counting laser altimetry, measurements obtained from the Multiple Altimeter Beam Experimental Lidar (MABEL) instrument are critical. In support of the ICESat-2 mission, this paper derives an algorithm for the detection of ground and vegetation canopy in photon-counting laser altimeter data. This approach uses a density-based clustering model and modifies the shape of search area. Based on results from MABEL observations, the proposed approach is seen to be robust in detecting ground and vegetation canopy as well as background noise reduction. In addition, this approach can be quickly implemented and adaptive to photon-counting lidar data sets with different point cloud densities.