Image of Photogrammetric rockfall monitoring in Alpine environments using M3C2 and tracked motion vectors

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Photogrammetric rockfall monitoring in Alpine environments using M3C2 and tracked motion vectors

This paper introduces methods for monitoring rock slope movements in Alpine environments based on terrestrial images. The first method is a photogrammtric point cloud-based deformation analysis, relying on M3C2. Although effective in identifying large changes, the method has a tendency to underestimate smaller-scale movements. A feature-based method is presented to address this limitation, using SIFT features to track keypoints in images from different epochs. These automatically detected 3D vectors offer high spatial density and enable small-scale movement detection in the order of a few millimeters. The results are incorporated into a deformation analysis that allows statistically based conclusions about the ongoing movements. The workflow relies on georegistration using Ground Control Points. To investigate the possibility of avoiding these points, a registration method based on the ICP algorithm and M3C2 is tested. The study utilizes data from an active landslide site at Hochvogel Mountain in the Alps, analyzing changes and deformations from 2018 to 2021, revealing an average motion of 75 mm.

Ketersediaan
58621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
16 hlm PDF, 17.734 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.12, April 2024
Subjek
Point Cloud
Deformation analysis
ICP algorithm
SIFT features
SfM
Rock slope failure
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Photogrammetric rockfall monitoring in Alpine environments using M3C2 and tracked motion vectors
    This paper introduces methods for monitoring rock slope movements in Alpine environments based on terrestrial images. The first method is a photogrammtric point cloud-based deformation analysis, relying on M3C2. Although effective in identifying large changes, the method has a tendency to underestimate smaller-scale movements. A feature-based method is presented to address this limitation, using SIFT features to track keypoints in images from different epochs. These automatically detected 3D vectors offer high spatial density and enable small-scale movement detection in the order of a few millimeters. The results are incorporated into a deformation analysis that allows statistically based conclusions about the ongoing movements. The workflow relies on georegistration using Ground Control Points. To investigate the possibility of avoiding these points, a registration method based on the ICP algorithm and M3C2 is tested. The study utilizes data from an active landslide site at Hochvogel Mountain in the Alps, analyzing changes and deformations from 2018 to 2021, revealing an average motion of 75 mm.
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