Assessment of Sea-Ice Classification Capabilities during Melting Period Using Airborne Multi-Frequency PolSAR Data

• Published in: Remote Sensing
• Main Authors: Peng Wang, Xi Zhang, Lijian Shi, Meijie Liu, Genwang Liu, Chenghui Cao, Ruifu Wang
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: polarimetric Synthetic Aperture Radar; multi-frequency; polarimetric feature; Bohai Sea; melting period; sea-ice classification; Science; Q; Sea ice
• Online Access: https://doi.org/10.3390/rs16061100; https://doaj.org/article/9ef7d54da7f441f6b4a1a562802ef6b0

Sea-ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, primarily due to meltwater complicating the distinguishability of sea-ice types. In response to this issue, this study introduces a novel method for classifying sea ice during the Bohai Sea’s melting period. The method categorizes sea ice into five types: open water (OW), gray ice (Gi), melting gray ice (GiW), gray–white Ice (Gw), and melting gray–white Ice (GwW). To achieve this classification, 51 polarimetric features are extracted from L-, S-, and C-band PolSAR data using various polarization decomposition methods. This study assesses the separability of these features among different combinations of sea-ice type by calculating the Euclidean distance (ED). The Support Vector Machine (SVM) classifier, when employed with single-frequency polarimetric feature sets, achieves the highest accuracy for OW and Gi in the C-band, GiW in the S-band, and Gw and GwW in the L-band. Remarkably, the C-band features exhibit the overall highest accuracy when compared to the L-band and S-band. Furthermore, employing a multi-dimensional polarimetric feature set significantly improves classification accuracy to 94.55%, representing a substantial enhancement of 9% to 22% compared to single-frequency classification. Benefiting from the performance advantages of Random Forest (RF) classifiers in handling large datasets, RF classifiers achieve the highest classification accuracy of 95.84%. The optimal multi-dimensional feature composition includes the following: L-band: SE, SE I , <semantics> α ¯ </semantics> , Span; S-band: SE I , SE, Span, P V-Freeman , λ 1 , λ 2 C-band: SE, SE I , Span, λ 3 , P V-Freeman . The results of this study provide a reliable new method for future sea-ice monitoring during the melting season.