DFC-Net: Dual-Branch Collaborative Feature Enhancement for Cloud Detection in Remote Sensing Images

High-precision cloud detection is crucial for the interpretation of remote sensing images. Although deep learning-based methods have achieved remarkable results, challenges remain in the fine-grained segmentation of cloud boundaries and the detection of thin clouds over complex land surfaces. To this end, this paper proposes a dual-branch co-optimization network (DFC-Net), which achieves cross-scale feature fusion and detail enhancement through the dual-branch interaction mechanism of global context modeling and local detail sensing. In the network architecture, the Residual Fusion Module (ResFM) is designed to construct an enhanced global feature pyramid, which then fuses with the decoder feature to mitigate spatial information loss and strengthen global representation. In addition, the multilevel jump-connection fusion strategy adopted at the decoding end achieves efficient integration of low-level texture features, which significantly improves the model's ability to characterize the fine structure of cloud boundaries. Experimental results on the HRC_WHU and 95-Cloud datasets show that DFC-Net outperforms existing state-of-the-art methods in all performance metrics, and especially exhibits excellent performance advantages in cloud boundary segmentation and thin cloud detection tasks.