Semi-Supervised Landcover Classification with Adaptive Pixel-Rebalancing Self-Training

Semi-supervised learning methods can assist in making use of extensive existing data resources while lowering the cost of manual labeling, which is significant for visual scene under-standing. Previous works on semi-supervised semantic segmentation have paid little attention to class imbalance, leading to the aggravation of the long-tail effect already present in the training data. To address this problem, we propose a novel method adaptive pixel-rebalancing self-training (APRST), which rebalances training data by adaptively sampling at the pixel level, thereby alleviating the class imbalance. Based on APRST, we further design a multi-models with cross pseudo supervision scheme, denoted as APRST+, which alleviates the confirmation bias problem and improves the quality of pseudo annotations. In addition, the segmentation performance is further improved by simply utilizing the DEM data for threshold filtering. Experiment results show that our approach achieves the state-of-the-art in the DFC2022 Track SLM test phase (mIOU of 0.5335).