Han Yanling, Huang Junjie, Ma Zhenling, Zheng Bowen, Wang Jing, Zhang Yun
Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China.
Sensors (Basel). 2024 Apr 29;24(9):2836. doi: 10.3390/s24092836.
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 determined using Sentinel-1 data.
海冰作为地球生态系统的重要组成部分，因其厚度对全球气候和人类活动有着深远影响。因此，海冰厚度反演具有重要的研究意义。由于环境和设备相关的限制，目前可用于遥感反演的样本数量不足。在高空间分辨率下，遥感数据包含的信息有限且存在噪声干扰，严重影响海冰厚度反演的准确性。针对上述问题，我们利用波弗特海的吃水深度数据进行了实验，并设计了一种改进的梯度提升决策树（GBDT）方法，该方法集成了特征增强和主动学习策略（IFEAL - GBDT）。在这种方法中，利用入射角和时间序列对数据进行时空校正，减少时间和空间影响。同时，基于原始极化信息生成有效的多属性特征，以扩展信息内容并提高不同厚度海冰的可分离性。考虑到海冰的生长周期和年龄，添加了月份和海水温度的属性。此外，我们研究了一种基于最大标准差的主动学习策略，以选择更多信息丰富且具有代表性的样本，并提高模型的泛化能力。改进后的GBDT模型用于训练和预测，在处理非线性、高维数据以及数据噪声问题方面具有优势，进一步扩展了特征增强和主动学习策略的有效性。与其他方法相比，本文提出的方法实现了最佳反演精度，IFEAL - GBDT的平均绝对误差为8厘米，均方根误差为13.7厘米，相关系数为0.912。本研究证明了我们方法的有效性，适用于利用哨兵 - 1数据进行海冰厚度的高精度反演。
