  Remote sensing change detection (RSCD) aims to identify the changes of
interest in a region by analyzing multi-temporal remote sensing images, and has
an outstanding value for local development monitoring. Existing RSCD methods
are devoted to contextual modeling in the spatial domain to enhance the changes
of interest. Despite the satisfactory performance achieved, the lack of
knowledge in the frequency domain limits the further improvement of model
performance. In this paper, we propose DDLNet, a RSCD network based on
dual-domain learning (i.e., frequency and spatial domains). In particular, we
design a Frequency-domain Enhancement Module (FEM) to capture frequency
components from the input bi-temporal images using Discrete Cosine Transform
(DCT) and thus enhance the changes of interest. Besides, we devise a
Spatial-domain Recovery Module (SRM) to fuse spatiotemporal features for
reconstructing spatial details of change representations. Extensive experiments
on three benchmark RSCD datasets demonstrate that the proposed method achieves
state-of-the-art performance and reaches a more satisfactory
accuracy-efficiency trade-off. Our code is publicly available at
https://github.com/xwmaxwma/rschange.
