山崩
流离失所(心理学)
摄影测量学
地质学
遥感
匹配(统计)
光流
数字图像相关
大地测量学
计算机科学
流量(数学)
一致性(知识库)
曲面(拓扑)
卫星
计算机视觉
算法
人工智能
图像(数学)
光学
数学
几何学
物理
地貌学
统计
心理学
天文
心理治疗师
作者
Ellorine Carle,Pascal Sirguey,Simon C. Cox
标识
DOI:10.1016/j.cageo.2023.105378
摘要
This study proposes and evaluates the performance of two image matching algorithms applied to hillshades derived from consecutive high-resolution digital surface models (DSMs) to measure surface displacements on landslides. The method is applied on Te Horo, a slow-moving landslide in the Dart Valley of New Zealand’s Southern Alps/Kā Tiritiri o te Moana using pairs of hillshades derived from Airborne and Satellite Photogrammetric Mapping (APM/SPM) of imagery captured in 2018 and 2020. This novel approach uses the consistency of displacement predictions generated from multiple hillshade pairs to gauge match quality and mask unreliable displacement predictions. The study compares a widely used normalised cross-correlation algorithm (NCC) alongside an optical flow approach for image matching. The performances of both algorithms are assessed against manually derived displacements of prominent surface features. We demonstrate the effectiveness of the masking approach as well as the good performance of the optical flow algorithm in delivering dense, accurate displacement measurements efficiently, particularly when high resolution DSMs are available. The results show that the main translational body of the landslide was displaced at rates averaging 25–55 mm day−1 over the 2018–2020 period.
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