Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring

IF 2.1 4区 地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Francesco Ioli, Niccolò Dematteis, Daniele Giordan, Francesco Nex, Livio Pinto
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Abstract

Short-term monitoring of alpine glaciers is crucial to understand their response to climate change. This paper presents a low-cost multi-camera system tailored for 4D glacier monitoring using deep learning stereo-photogrammetry. Our approach integrates multi-temporal 3D reconstruction from stereo cameras and surface velocity estimation from a monoscopic camera through digital image correlation. To address the challenges posed by wide camera baselines in complex environments, we have integrated state-of-the-art deep learning feature matching algorithms into ICEpy4D, a Python toolkit designed for 4D monitoring (https://github.com/franioli/icepy4d). In a pilot study conducted on the debris-covered Belvedere Glacier (Italian Alps), our stereoscopic setup, with a camera base–height ratio close to one, captured daily images from May to November 2022. Our approach utilized SuperPoint and SuperGlue for feature matching, resulting in a daily 3D reconstruction of the glacier terminus, as traditional SIFT-like feature matching fails in this scenario. Using dense point clouds with decimetric accuracy, we estimated daily ice volume loss and glacier retreat at the terminus. The total ice volume loss was \(63\,000\,\text{m}\)\({}^{3}\) and the retreat was \(17.8\,\text{m}\). Surface kinematics revealed three times higher surface velocity during the warm season (May–September) than in the fall (September–November). Daily analyses revealed a significant short-term correlation between air temperature, glacier surface velocity and ice ablation, providing insight into the glacier’s response to external forces. The low cost and ease of deployment of the proposed system facilitates replication at other sites for short-term monitoring of glacier dynamics.

Abstract Image

用于 4D 短期冰川动态监测的深度学习低成本摄影测量技术
对高山冰川进行短期监测对于了解它们对气候变化的反应至关重要。本文介绍了利用深度学习立体摄影测量技术为四维冰川监测量身定制的低成本多相机系统。我们的方法整合了立体相机的多时空三维重建和单镜相机通过数字图像关联进行的表面速度估算。为了应对复杂环境中宽相机基线带来的挑战,我们将最先进的深度学习特征匹配算法集成到了专为四维监测设计的 Python 工具包 ICEpy4D 中 (https://github.com/franioli/icepy4d)。在对意大利阿尔卑斯山被碎石覆盖的贝尔维德雷冰川(Belvedere Glacier)进行的试点研究中,我们的立体设置(相机基高比接近1)捕获了2022年5月至11月期间的每日图像。我们的方法利用 SuperPoint 和 SuperGlue 进行特征匹配,从而实现了冰川终点的每日三维重建,因为传统的 SIFT 类特征匹配在这种情况下会失效。利用分米精度的高密度点云,我们估算了冰川终点的每日冰量损失和冰川退缩。总冰量损失为(63,000\text{m})\({}^{3}\),冰川退缩为(17.8\text{m})。地表运动学显示,暖季(5月至9月)的地表速度是秋季(9月至11月)的三倍。每日分析表明,气温、冰川表面速度和冰消融之间存在明显的短期相关性,这为了解冰川对外力作用的反应提供了依据。拟议系统成本低,易于部署,便于在其他地点复制,以对冰川动态进行短期监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.20
自引率
2.40%
发文量
38
期刊介绍: PFG is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the interconnected field of geoinformation science. It places special editorial emphasis on the communication of new methodologies in data acquisition and new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general. The journal hence addresses both researchers and students of these disciplines at academic institutions and universities as well as the downstream users in both the private sector and public administration. Founded in 1926 under the former name Bildmessung und Luftbildwesen, PFG is worldwide the oldest journal on photogrammetry. It is the official journal of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).
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