Semantic Segmentation for Multi-Scene Remote Sensing Images with Noisy Labels Based on Uncertainty Perception

Xinran Lyu, Libao Zhang

Abstract: As the annotation of remote sensing images requires domain expertise, it is difficult to construct a large-scale and accurate annotated dataset. Image-level annotation data learning has become a research hotspot. In addition, due to the difficulty in avoiding mislabeling, label noise cleaning is also a concern. In this paper, a semantic segmentation method for remote sensing images based on uncertainty perception with noisy labels is proposed. The main contributions are three-fold. First, a label cleaning method based on iterative learning is presented to handle noise labels such as missing or incorrect annotations. Second, a two-stage semantic segmentation model is proposed for image-level annotation, which eliminates the need for post-processing steps during testing. Lastly, a complementary uncertainty perception function is introduced to improve the utilization of dataset features and enhance the accuracy of segmentation. The effectiveness of this method was verified through comprehensive evaluation with 7 models on four datasets.