Combined pattern matching and feature tracking for Bohai Sea ice drift detection using Gaofen-4 imagery

ABSTRACT

Sea ice is the main cause of winter marine disasters in the Bohai Sea, which is an important sea area in China. The sea ice in the Bohai Sea is easily deformed by strong water currents and rapid temperature changes. Consequently, low spatial and temporal resolution data cannot meet the needs of high-precision monitoring of the sea ice in the Bohai Sea. The geostationary satellite Gaofen-4 (GF-4) has the unique advantages of a high temporal resolution (20 s) and a high spatial resolution (50 m) and can be used to monitor sea ice drift in the Bohai Sea. We evaluate a new algorithm that combines the scale-invariant feature transform (SIFT) technique and the normalized cross-correlation (NCC) method to obtain accurate sea ice drift information. The interclass separability method is used to determine the optimal band combination (OBC), and the results show that the OBC for distinguishing sea ice from sea water is B₂×(B₁× B₅)⁻¹. The proposed method improves noise immunity and calculation efficiency and considers the characteristics of ice blocks. The proposed algorithm can also obtain more correctly matched feature points than the NCC method and approximately 10 times more correct points than the SIFT technique. The mean absolute error (MAE) of the velocity obtained with the proposed algorithm is 0.1615 m s⁻¹ lower than those obtained with the SIFT and NCC methods, and the root mean square error of the velocity determination of the three algorithms is very small. The MAE of the ice movement direction obtained with the proposed algorithm is 0.605° lower than those obtained with the SIFT and NCC methods, and the MAE of a second data set is 12.378° better than that of the SIFT method. The motion of sea ice obtained by the proposed algorithm is close to the actual movement of the sea ice in the Bohai Sea.

Acknowledgements

The authors would like to thank the China Centre For Resources Satellite Data and Application for providing the GF-4 satellite data. We gratefully acknowledge the detailed comments of two anonymous reviewers, which helped to considerably improve the readability of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Key Research and Development Program of China under grant [2017YFC1405005, 2018YFC1407203]., in part by the National Nature Science Foundation of China under grant [41976173], and in part by the Ministry of Science and Technology of China and the European Space Agency through the Dragon-4 Program under grant 32292.