GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies—Sea Ice Thickness Inversion in Beaufort Sea

• Published in: Sensors
• Main Authors: Yanling Han, Junjie Huang, Zhenling Ma, Bowen Zheng, Jing Wang, Yun Zhang
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: sea ice thickness; active learning; query strategy; GBDT; feature enhancement; sentinel-1; Chemical technology; TP1-1185; Beaufort Sea; Sea ice
• Online Access: https://doi.org/10.3390/s24092836; https://doaj.org/article/b773a83804f5434485ef08149ebbf8d4

Sea ice, as an important component of the Earth’s ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model’s generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness ...