We introduced an effective preprocessing method based on Sentinel-1 remote-sensing data to obtain a more accurate dataset and proposed a triangulation-based feature-tracking and pattern-matching algorithm. By establishing a triangular network, the advantages of the two algorithms were effectively combined, which not only improved the efficiency but also enhanced the spatial-distribution uniformity of the sea-ice drift vectors. Additionally, this study investigated the applicability of the algorithm for strong-noise areas of like-polarized (HH) and cross-polarized (HV) data. The experimental results show that sea-ice drift vectors obtained using this algorithm exhibit a high coverage and reduce the root mean square error by ~10%, thereby improving the detection accuracy and robustness against noise. Furthermore, the detection accuracy remains as high as 98%, even in the presence of interference by strip noise. These results demonstrate the effectiveness of this method for effectively monitoring sea-ice drift.