An improvement in accuracy and spatial resolution of the pattern-matching sea ice drift from SAR imagery

Sea ice drift is a crucial parameter for sea ice flux, atmospheric and ocean circulation, and ship navigation. Pattern matching is widely used to retrieve sea ice drift from Synthetic Aperture Radar (SAR) data, but it often yields mismatched vectors and coarse spatial resolution. This study presents...

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Bibliographic Details
Published in:International Journal of Digital Earth
Main Authors: Xue Wang, Zhuoqi Chen, Yan Fang, Ruirui Wang, Chaoyue Li, Gang Li, Fengming Hui, Huabing Huang, Xiao Cheng
Format: Article in Journal/Newspaper
Language:English
Published: Taylor & Francis Group 2023
Subjects:
sea ice drift
pattern matching
sar
feature tracking
fusion
Mathematical geography. Cartography
GA1-1776
Sea ice
Online Access:https://doi.org/10.1080/17538947.2023.2264918
https://doaj.org/article/d5c7b9ea96cb4ef6bff75fa36404ed2e
Description
Summary:Sea ice drift is a crucial parameter for sea ice flux, atmospheric and ocean circulation, and ship navigation. Pattern matching is widely used to retrieve sea ice drift from Synthetic Aperture Radar (SAR) data, but it often yields mismatched vectors and coarse spatial resolution. This study presents a framework to enhance the spatial resolution and accuracy of pattern-matching sea ice drift derived from SAR images. The framework employs the Accelerated-KAZE feature extraction method and Brute-Force feature matcher to extract feature-tracking sea ice drift vectors from SAR data, with mismatched vectors subsequently removed. The pattern-matching vectors are then refined by fusing with these feature-tracking vectors, using a Co-Kriging algorithm. Using the sea ice drift product from the Technical University of Denmark space as the pattern-matching vector field for refinement, the framework's effectiveness is evaluated by comparing the refined vectors with buoy displacements and pattern-matching vectors across five selected regions. Results show a reduction in velocity and direction root mean square error (RMSE) by 0.47 km/d (22%) and 4.97° (28%), respectively, and an enhanced spatial resolution from 10 km to 1 km. The findings demonstrate the framework's success in improving the accuracy and resolution of pattern-matching sea ice drift from SAR imagery.

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