Deep-Learning-Based Sea Ice Classification With Sentinel-1 and AMSR-2 Data

In the era of big data, how to utilize synthetic aperture radar (SAR) and passive microwave radiometer data for better sea ice monitoring by deep-learning technology has recently attracted wide attention. In this article, we first propose a universal and lightweight multiscale cascade network (MCNet) for Sentinel-1 SAR-based sea ice classification. In comparison with the previous local inference methods that split SAR images to small patches, our proposed global inference method MCNet is able to segment whole SAR images directly. Then, taking MCNet as a basis, we investigate four different fusion methods for Sentinel-1 SAR and the advanced microwave scanning radiometer-2 data. These are the early fusion, deep fusion, late fusion, and the hybrid method, which fuse data at the input level, feature level, decision level, as well as both feature and decision levels, respectively. Experiments demonstrate that MCNet performs better than the commonly used U-Net in terms of accuracy, memory usage, inference speed, and in capturing small-scale local details. As for data fusion, compared with MCNet, significant improvements have been achieved for all data fusion methods, except the early fusion method. Both deep fusion and late fusion methods have their own advantages in classifying certain sea ice types. By combining them together, the proposed hybrid method achieves optimal performance. Finally, with regard to the class imbalance problem, we recommend the application of self-supervised learning to mine the value of massively unlabeled SAR images.