基于深度学习的光流在海冰动力学精细形变映射中的应用

IF 4.6 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Matias Uusinoka, Jari Haapala, Arttu Polojärvi
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引用次数: 0

摘要

用于研究物体运动和变形的光学方法往往难以分辨隐藏在观测噪声背后的微小位移。在地球物理应用中,这限制了对较低空间和时间分辨率的分析,而要想了解材料变形和失效,就必须可靠地提取高分辨率数据。在这项工作中,我们提出了一种新方法,利用基于深度学习的光流来确定噪声观测数据的变形。为了提高估计精度,我们引入了一种考虑上下文信息的新型初始化技术。这样就能以前所未有的高分辨率描述雷达图像中的运动。我们在验证案例中使用了所提出的技术,与目前使用的方法和对海冰变形的船舶雷达观测进行了比较。我们的工作成果是一个开源的端到端工具,用于确定具有小像素位移和高观测噪声的数据集的全场拉格朗日变形场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep Learning-Based Optical Flow in Fine-Scale Deformation Mapping of Sea Ice Dynamics

Deep Learning-Based Optical Flow in Fine-Scale Deformation Mapping of Sea Ice Dynamics

Deep Learning-Based Optical Flow in Fine-Scale Deformation Mapping of Sea Ice Dynamics

Deep Learning-Based Optical Flow in Fine-Scale Deformation Mapping of Sea Ice Dynamics

Deep Learning-Based Optical Flow in Fine-Scale Deformation Mapping of Sea Ice Dynamics

Optical methods deployed for studying motion and deformation of objects often struggle to distinguish small displacements hidden behind observational noise. In geophysical applications, this has limited analysis to lower spatial and temporal resolutions, while reliable extraction of high-resolution data is required for understanding material deformation and failure. In this work, we propose a novel method for determining deformation for noisy observational data using deep learning-based optical flow. To enable higher estimate accuracy, we introduce a novel initialization technique considering contextual information. This allows an unprecedentedly high-resolution description of motion in radar imagery. We use the proposed technique on verification cases to compare with the currently used methodologies and on ship radar observations on sea ice deformation. The outcome of our work is an open-source end-to-end tool for determining full-field Lagrangian deformation fields for data sets with small pixel displacements and high observational noise.

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来源期刊
Geophysical Research Letters
Geophysical Research Letters 地学-地球科学综合
CiteScore
9.00
自引率
9.60%
发文量
1588
审稿时长
2.2 months
期刊介绍: Geophysical Research Letters (GRL) publishes high-impact, innovative, and timely research on major scientific advances in all the major geoscience disciplines. Papers are communications-length articles and should have broad and immediate implications in their discipline or across the geosciences. GRLmaintains the fastest turn-around of all high-impact publications in the geosciences and works closely with authors to ensure broad visibility of top papers.
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