Sensors, Vol. 24, Pages 2836: GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies & mdash;Sea Ice Thickness Inversion in Beaufort Sea

Sensors, Vol. 24, Pages 2836: GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies—Sea Ice Thickness Inversion in Beaufort Sea Sensors doi: 10.3390/s24092836 Authors: Han Huang Ma Zheng Wang Zhang 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 temperat...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research