Application of Feature Tracking Using K-Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking

A feature-tracking algorithm algorithm utilizing the proposed
K
-nearest-neighbor vector field consensus (KVFC) to filter outliers is developed to monitor the dynamic changes of sea ice retrieval from synthetic aperture radar (SAR) images. The KVFC is based on vector field consensus and combines with local neighborhood correspondences to optimize the elimination of outliers while retaining inliers as many as possible. The proposed KVFC was evaluated and compared with several algorithms on three standard datasets and Sentinel-1 image pairs in the Fram Strait and the Beaufort Sea. The KVFC obtained more sea-ice drift vectors than the nearest neighbor similarity ratio (NNSR) with a 0.7 threshold and generated dense distribution of sea-ice drift vectors combined with the HH and HV channels. Using buoy datasets to calculate the sea-ice drift speed and evaluate algorithm performance, the proposed approach yielded a lower mean error (KVFC: −0.150 cm/s, NNSR: 0.407 cm/s), lower root mean square error (KVFC: 0.476 cm/s, NNSR: 1.817 cm/s), and lower angle deviation (KVFC: 3.542°, NNSR: 10.318°) compared to the NNSR.