PROGRESSIVE REFINEMENT LEARNING BASED ON FEATURE INTERACTIVE FUSION FOR SEMANTIC SEGMENTATION OF REMOTE SENSING LIMITED DATASET

Due to the labor cost and the accuracy of manual identification, it is very difficult to make a strong label dataset of remote sensing images with a large amount of data. Therefore, the limited remote sensing dataset has become a research hotspot in recent years. However, due to insufficient precision and the lack of label accuracy, these methods often have insufficient expression ability. In this paper, we proposed a semantic segmentation method for remote sensing images by progressive refinement learning. Firstly, we construct multiple classification networks to vote for label noise cleaning, and select a network to retrain. Then, the method based on hierarchical feature learning is used to realize the pixel-level pseudo label calculation. Secondly, we proposed to construct feature interactive fusion module in the multi-level codec to achieve image group semantic segmentation. Comprehensive evaluations and the comparison with 7 methods validate the superiority of the proposed model.