Aleatoric Uncertainty Embedded Transfer Learning for SEA-ICE Classification in SAR Images

Ying Liu, Zhongling Huang, Junwei Han

Abstract: Fine-grained sea-ice classification in SAR images is challenging due to the scarce labeled data and the imperfect annotation. Pre-training strategies are commonly carried out to prevent severe overfitting with limited labeled data. In spite of this, the observation noise still exists in the transferred features, which can be captured by aleatoric uncertainty. In this paper, we propose an aleatoric uncertainty embedded sea-ice classification method together with transfer learning of two different pre-training strategies. Instead of representing the transferred feature as a deterministic embedding, the proposed method concerns the feature uncertainty and models the embedding as a Gaussian distribution with variance. The experiments demonstrate that the proposed aleatoric uncertainty estimation is beneficial to improving the classification result of transfer learning. Based on the measured feature uncertainty, we analyze the potential of integrating two different pre-trained models to further enhance the performance.