Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network

• Published in: Remote Sensing
• Main Authors: Hongyang Wan, Xiaowen Luo, Ziyin Wu, Xiaoming Qin, Xiaolun Chen, Bin Li, Jihong Shang, Dineng Zhao
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: sea ice; classification; SAR; polarization decomposition; JTFA; multi-feature; Science; Q; Sea ice
• Online Access: https://doi.org/10.3390/rs15164014; https://doaj.org/article/161cfdf79eee4d648379ad443f25b2f0

Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method.