Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking

Abstract

Applying feature tracking techniques to SAR imagery generates high resolution sea ice motion fields. However, the bad matching vectors still exist after the NNDR (Nearest Neighbor Distance Ratio) test and contaminate the derived motion fields, which need to be identified and filtered out. In this research, we propose two algorithms to eliminate such wrong matching vectors. The first employs the matching results derived by the maximum cross-correlation (MCC) method as the reference motion fields to evaluate such wrong matches. The second method employs the local spatial consistency presumption of sea ice motion fields. A Voronoi diagram is applied to slice the overlapping area of two SAR images into many fractions, and each fraction extends its size 50% outward to calculate the regional mean sea ice flow vector and standard deviation. Any vector within the fraction that exceeds 3 times the regional standard deviation will be recognized as an outlier and filtered out. Two methods are tested to two cases with strong rotation or irregular sea ice motion fields derived from Sentinel-1 imagery. The overall accuracy of our two methods is 93.9% and 98.7%, and they sacrifice 6.12% /1.22% of the correct vectors to filter out 100.0% / 94.12% of the wrong vectors for the MCC referenced filter and Voronoi fragmented filter, respectively.

Retrieval of Arctic Sea Ice Motion from FY-3D/MWRI Brightness Temperature Data

“…An open-source SIM retrieval method combining template matching and feature tracking was provided in 2017 [24], and it was used to retrieve SIM from the Sentinel-1 SAR data in parts of the Fram Strait. In 2022, Li et al retrieved SIM using Sentinel-1 SAR data in parts of the Arctic with a feature tracking algorithm and corrected the results using a different vector filter [25]. Another important research field related to SIM is the validation of SIM data product quality.…”