The loss of high-frequency details and the insufficient fusion of global and local features remain key challenges in bi-temporal remote sensing image change captioning. To address these issues, a change captioning method based on a frequency-spatial dual-domain mamba network was proposed. A frequency-spatial dual-domain perception module was first constructed, through which global and local features are efficiently integrated via a collaborative mechanism operating across the frequency and spatial domains. Subsequently, a fourier convolution enhancement module was developed, in which fast Fourier convolution is employed to dynamically select critical frequency components, thereby improving the representation of high-frequency details. By concatenating frequency-and spatial-domain features, both high-frequency detail preservation and enhanced global perception were achieved. Finally, a spatial-adaptive gating module was introduced to regulate convolutional kernel weight distributions through spatially adaptive coordinate transformations. Connected in parallel with the Fourier enhancement branch, this module further strengthens the dynamic fusion between local features and global contextual information. Experimental results on the LEVIR-CC dataset show that BLEU-4 and CIDEr-D scores of 66.06% and 139.08% are achieved, demonstrating clear advantages in accuracy and fine-grained description quality for remote sensing image change captioning.