CAMformer: A Single-Stage CNN–Transformer Hybrid for Weakly Supervised Semantic Segmentation in Aerial Imagery

Weakly supervised semantic segmentation (WSSS) with image-level labels has emerged as a cost-effective solution for large-scale remote sensing (RS) imagery, yet CNN-based approaches often suffer from limited receptive fields, incomplete activations, and background confusion in complex multi-class scenes. Transformer-based methods alleviate these issues by modeling long-range dependencies, but many existing designs are multi-stage and inefficient, or fail to adequately address noise and contextual bias in RS data. This work proposes CAMformer, a single-stage hybrid CNN–Transformer framework that jointly captures local details and global semantics for more reliable pseudo-label generation. To further improve robustness, we introduce an inter-class decoupling strategy (ICD) to resolve category conflicts, an uncertainty-weighted optimization (UWO) scheme to suppress noisy activations, and a context intervention sample enhancement (CISE) module to mitigate biased contextual dependencies. Extensive experiments on the challenging iSAID and ISPRS Vaihingen datasets demonstrate that CAMformer achieves state-of-the-art performance with 39.5% and 46.4% mIoU respectively, surpassing recent one-stage methods, while cross-domain validation on PASCAL VOC2012 confirms its generalization to a certain extent. These results highlight the effectiveness of combining CNN–Transformer hybrids with context-aware learning for WSSS in RS applications.