Towards Reliable Sea Ice Drift Estimation in the Arctic Deep Learning Optical Flow on RADARSAT-2

• URL: http://arxiv.org/abs/2510.26653v1
• Date: Thu, 30 Oct 2025 16:20:28 GMT
• Title: Towards Reliable Sea Ice Drift Estimation in the Arctic Deep Learning Optical Flow on RADARSAT-2
• Authors: Daniela Martin, Joseph Gallego,
• Abstract summary: We present the first large scale benchmark of 48 deep learning optical flow models on RADARSAT 2 sea ice imagery.<n>Several models achieve sub kilometer accuracy (EPE 6 to 8 pixels, 300 to 400 m), a small error relative to spatial scales of sea ice motion and typical navigation requirements in the Arctic.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate estimation of sea ice drift is critical for Arctic navigation, climate research, and operational forecasting. While optical flow, a computer vision technique for estimating pixel wise motion between consecutive images, has advanced rapidly in computer vision, its applicability to geophysical problems and to satellite SAR imagery remains underexplored. Classical optical flow methods rely on mathematical models and strong assumptions about motion, which limit their accuracy in complex scenarios. Recent deep learning based approaches have substantially improved performance and are now the standard in computer vision, motivating their application to sea ice drift estimation. We present the first large scale benchmark of 48 deep learning optical flow models on RADARSAT 2 ScanSAR sea ice imagery, evaluated with endpoint error (EPE) and Fl all metrics against GNSS tracked buoys. Several models achieve sub kilometer accuracy (EPE 6 to 8 pixels, 300 to 400 m), a small error relative to the spatial scales of sea ice motion and typical navigation requirements in the Arctic. Our results demonstrate that the models are capable of capturing consistent regional drift patterns and that recent deep learning based optical flow methods, which have substantially improved motion estimation accuracy compared to classical methods, can be effectively transferred to polar remote sensing. Optical flow produces spatially continuous drift fields, providing motion estimates for every image pixel rather than at sparse buoy locations, offering new opportunities for navigation and climate modeling.

Related papers

• Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting [0.0]
  We adapt a deep learning model to predict sea temperature (SST) in the Canary Upwelling System.<n>By fine-tuning this model with high-resolution oceanographic reanalysis data, we demonstrate its ability to capture complex patterns.<n>The model successfully reproduces large-scale SST structures but faces challenges in capturing finer details in coastal regions.
  arXiv  Detail & Related papers  (2025-10-29T14:30:12Z)
• Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions [48.529493393948435]
  The visible-light camera has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems. The visual imaging quality inevitably suffers from several kinds of degradations under complex weather conditions. We develop a general-purpose multi-scene visibility enhancement method to restore degraded images captured under different weather conditions.
  arXiv  Detail & Related papers  (2024-09-02T23:46:27Z)
• Attenuation-Aware Weighted Optical Flow with Medium Transmission Map for Learning-based Visual Odometry in Underwater terrain [0.03749861135832072]
  This paper addresses the challenge of improving learning-based monocular visual odometry (VO) in underwater environments. The novel wflow-TartanVO is introduced, enhancing the accuracy of VO systems for autonomous underwater vehicles (AUVs) Evaluation of different real-world underwater datasets demonstrates the outperformance of wflow-TartanVO over baseline VO methods.
  arXiv  Detail & Related papers  (2024-07-18T05:00:15Z)
• Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance [1.4659076103416173]
  We present an advanced convolutional neural network (CNN) architecture for ship classification based on optical satellite imagery. We first incorporated a standard CBAM to direct the model's focus toward more informative features, achieving an accuracy of 87%. This model demonstrated a remarkable accuracy of 95%, with precision, recall, and F1 scores all witnessing substantial improvements across various ship classes.
  arXiv  Detail & Related papers  (2024-04-02T17:48:46Z)
• Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble [0.3626013617212667]
  We propose an adaptive surrogate modeling approach named LANE-SI. It uses ensemble of relatively simple deep learning models with different loss functions for forecasting of sea ice concentration in the specified water area. We achieve a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.
  arXiv  Detail & Related papers  (2023-12-07T14:48:30Z)
• MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting [0.31410342959104726]
  We propose MT-IceNet - a UNet based spatial and multi-temporal (MT) deep learning model for forecasting Arctic sea ice concentration (SIC) Our proposed model provides promising predictive performance for per-pixel SIC forecasting with up to 60% decrease in prediction error for a lead time of 6 months as compared to its state-of-the-art counterparts.
  arXiv  Detail & Related papers  (2023-08-08T18:18:31Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Optical Flow for Autonomous Driving: Applications, Challenges and Improvements [0.9023847175654602]
  We propose and evaluate training strategies to improve a learning-based optical flow algorithm. While trained with synthetic data, the model demonstrates strong capabilities to generalize to real world fisheye data. We propose a novel, generic semi-supervised framework that significantly boosts performances of existing methods in low light.
  arXiv  Detail & Related papers  (2023-01-11T12:01:42Z)
• DeepRM: Deep Recurrent Matching for 6D Pose Refinement [77.34726150561087]
  DeepRM is a novel recurrent network architecture for 6D pose refinement. The architecture incorporates LSTM units to propagate information through each refinement step. DeepRM achieves state-of-the-art performance on two widely accepted challenging datasets.
  arXiv  Detail & Related papers  (2022-05-28T16:18:08Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Sensor-Guided Optical Flow [53.295332513139925]
  This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy on known or unseen domains. We show how these can be obtained by combining depth measurements from active sensors with geometry and hand-crafted optical flow algorithms.
  arXiv  Detail & Related papers  (2021-09-30T17:59:57Z)
• End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera [81.66569124029313]
  We propose a camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field.
  arXiv  Detail & Related papers  (2020-06-07T08:18:31Z)
• Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization [59.9673626329892]
  We exploit the global relationship between optical flow and camera motion using epipolar geometry. We use implicit differentiation to enable back-propagation through the lower-level geometric optimization layer independent of its implementation.
  arXiv  Detail & Related papers  (2020-02-26T22:28:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.