Multi-Task Retrieval of Sea Ice Based on GNSS-R: An Integrated Framework Guided by Semi-Supervised Anomaly Detection

Sea ice plays a critical role in influencing various aspects of the atmosphere, ecology, oceans, and even human activities, making the task of sea ice retrieval (SIR) immensely important. Recent advancements have seen the application of machine learning methods based on Global Navigation Satellite System Reflectometry (GNSS-R) technology in the field of sea ice remote sensing. However, the existing algorithms are hindered by an over-reliance on labels, structural redundancies, and limited effectiveness in dealing with sample imbalances. To overcome these challenges, the Anomaly Detection Driven Semi-supervised Multi-task Retrieval Algorithm for Sea Ice based on GNSS-R is proposed in this paper. In the novel approach within the field of SIR, this study introduces the concept of semi-supervised anomaly detection. This method addresses the challenge posed by an over-reliance on labeled data and the issue of sample imbalance. The proposed algorithm is composed of two primary components: the data preprocessing module and the multi-task network structure Sea Ice Multi-Tasks Retrieval Network (SIMTRN) based on GNSS-R. The SIMTRN includes a sea ice detection (SID) module, which employs an implicit deep bootstrap mechanism for enhanced detection capabilities. Additionally, it encompasses a sea ice concentration retrieval (SICR) module that implements an innovative inter-module data feature sharing mechanism. This dual-module approach within a single network structure not only facilitates simultaneous execution of SID and SICR tasks but also effectively addresses and resolves the issue of structural redundancy. Experiments conducted on the datasets illustrate the superior SIR performance of the proposed method.