Recent advances in deep learning have substantially improved remote sensing change detection. However, most existing models still describe bi-temporal differences mainly from the spatial domain, making it difficult to fully capture complementary frequency domain cues in complex scenes. To address this limitation, this paper introduces a Spatial-Frequency Collaborative Learning Network (SFCLNet) for remote sensing change detection. In particular, hierarchical features are extracted from bi-temporal images using a Siamese backbone. A Spatial Domain Feature Fusion (SDFF) module is then designed to enhance local structural variation details by modeling the structural consistency between bi-temporal features. Meanwhile, a Frequency Domain Feature Fusion (FDFF) module is introduced to characterize frequency domain cues by separately modeling phase and amplitude components. Furthermore, a Spatial-Frequency Collaborative Fusion (SFCF) module is developed to obtain more discriminative change feature representations by integrating the fused spatial domain and frequency domain features in a channel-wise competitive way. Finally, the pixel-wise results are predicted using a UNet-based decoder that progressively aggregates the fused multi-level features. Experimental results on Google, LEVIR, and MSRS benchmark datasets show that SFCLNet achieves F1 scores of 88.94%, 91.39%, and 74.97%, respectively, outperforming several recently published methods. These results verify the effectiveness of jointly exploiting the frequency domain and spatial domain for remote sensing change detection.