Dominant Leaf Type Classification Using Sentinel-1 Time Series

The classification of dominant leaf types, which distinguishes forests based on their leaf conditions, is beneficial for forest management and policymakers. This paper proposes a model based on U-Net to classify the land into non-tree areas, broadleaf forests, and coniferous forests. The dual-pol Sentinel-1 data from January, May, August, and October of 2018 were stacked as a time series. Due to the class imbalance issue, where the non-tree area category dominates (52.69%) the dataset, the model tends to be biased. Thus, re-weighting is introduced to balance the loss. We tested and compared two types of methods: class-aware and task-aware re-weighting. The results indicate that re-weighting effectively mitigates the class imbalance issue.