Hazy Remote Sensing Image Semantic Segmentation with Weak Annotations via Pre-training Optimization and Co-training

In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate semantic segmentation. Despite the numerous haze removal methods developed for remote sensing images, their efficacy in the subsequent task of semantic segmentation remains inadequate. To address these issues, this paper aims to enhance the robustness of the segmentation network against haze interference by proposing a weakly supervised semantic segmentation framework based on pre-training optimization and dual-network co-training. The proposed approach employs a pseudo-label optimization network to effectively filter out noise interference and subsequently trains two parallel segmentation networks that mutually guide each other for enhanced robustness. Additionally, an edge optimization loss is introduced to improve prediction accuracy by incorporating both edge and texture information. Experimental comparisons with alternative methods across multiple datasets validate the superior performance of our proposed method.