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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Geomorphological Change Detection Using Object-Based Feature Extraction From Multi-Temporal LiDAR Data
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Geomorphological Change Detection Using Object-Based Feature Extraction From Multi-Temporal LiDAR Data

机译：利用多时态LiDAR数据中基于对象的特征提取进行地貌变化检测

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摘要

Multi-temporal LiDAR digital terrain models (DTMs) are used for the development and testing of a method for geomorphological change analysis in western Austria. Point data from two airborne LiDAR campaigns of 2003 and 2011 were filtered and interpolated into two 2m DTMs. Seven geomorphological features were mapped by using stratified object-based image analysis (OBIA) using terrain properties derived from the DTMs. Segmentation parameters and classification rules were set and applied to both data sets which allowed analysis of geomorphological change between 2003 and 2011. Volumetric change was calculated and summarized by their landform category. The multi-temporal landform classifications show where landforms changed into other landforms as the result of geomorphological process activity. However, differences in point densities and lack of data points below dense forest hindered accurate geomorphological change detection in these areas. When challenges related to interpolation techniques are tackled, stratified OBIA of multi-temporal LiDAR data sets is a promising tool for geomorphological change detection, and affiliated applications such as monitoring risk and natural hazards, rate of change analyses, and vulnerability assessments.
机译：多时间LiDAR数字地形模型（DTM）用于开发和测试奥地利西部地貌变化分析方法。来自2003年和2011年的两次机载LiDAR运动的点数据被过滤并插值到两个2m DTM中。通过使用基于DTM的地形属性的分层基于对象的图像分析（OBIA），绘制了七个地貌特征。设置了分割参数和分类规则，并将其应用于这两个数据集，从而可以分析2003年至2011年之间的地貌变化。根据地貌类别对体积变化进行了计算和总结。多时相地貌分类显示了由于地貌过程活动而使地貌转变为其他地貌的地方。但是，由于密度不均以及茂密的森林以下缺少数据点，这些地区的地貌变化难以准确检测。当解决与插值技术有关的挑战时，多时间LiDAR数据集的分层OBIA是一种很有前途的工具，可用于地貌变化检测以及相关应用程序，如监测风险和自然灾害，变化率分析和脆弱性评估。

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