MSAug: Multi-Strategy Augmentation for rare classes in semantic segmentation of remote sensing images

Abstract

Recently, remote sensing images have been widely used in many scenarios, gradually becoming the focus of social attention. Nevertheless, the limited annotation of scarce classes severely reduces segmentation performance. This phenomenon is more prominent in remote sensing image segmentation. Given this, we focus on image fusion and model feedback, proposing a multi-strategy method called MSAug to address the remote sensing imbalance problem. Firstly, we crop rare class images multiple times based on prior knowledge at the image patch level to provide more balanced samples. Secondly, we design an adaptive image enhancement module at the model feedback level to accurately classify rare classes at each stage and dynamically paste and mask different classes to further improve the model’s recognition capabilities. The MSAug method is highly flexible and can be plug-and-play. Experimental results on remote sensing image segmentation datasets show that adding MSAug to any remote sensing image semantic segmentation network can bring varying degrees of performance improvement.