This study proposes a novel method to joint utilization of visible and thermal infrared images from UAV perspectives. The method involves the development of a multimodal semantic segmentation model， termed CDFNet， which is designed based on cross-modal feature decoupling and attention refocusing. A cross-modal feature decoupling module is introduced to explicitly disentangle and enhance complementary discriminative features from different modalities， thereby improving the representational capacity of fused features in complex urban scenes. Furthermore， a focalizing attention decoder is incorporated to dynamically refine the attention scope towards small-scale objects during decoding， thereby effectively mitigating the interference from noisy backgrounds. Extensive experimentation on the Kust4K dataset demonstrates that CDFNet achieves mIoU improvements of 6.3% and 3.1% over the baseline and the current state-of-the-art multimodal method Sigma， respectively. Feature visualization and modality robustness evaluations further confirm that CDFNet yields more robust feature representations under low signal-to-noise conditions and significantly enhances segmentation accuracy for small targets in challenging urban road scenes from UAV perspectives.