Multi-Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation

To enhance the model’s generalization ability, here we implemented and compared three domain adaptation methods, i.e., the classic supervised fine-tuning method and two proposed unsupervised methods: Image StyleTransfer Domain Adaptation (ISTDA) and the Tversky Adversarial Domain Adaptation (TADA) network. In our proposed ISTDA, we uniformed the contextual information of multi-temporal images by Cycle Generative Adversarial Network (CycleGAN). We introduced the Tversky loss and the automatic adjustment of weights for multiple loss functions to suppress false positives and to improve the generalization of TADA.