  Change detection from synthetic aperture radar (SAR) imagery is a critical
yet challenging task. Existing methods mainly focus on feature extraction in
spatial domain, and little attention has been paid to frequency domain.
Furthermore, in patch-wise feature analysis, some noisy features in the
marginal region may be introduced. To tackle the above two challenges, we
propose a Dual-Domain Network. Specifically, we take features from the discrete
cosine transform domain into consideration and the reshaped DCT coefficients
are integrated into the proposed model as the frequency domain branch. Feature
representations from both frequency and spatial domain are exploited to
alleviate the speckle noise. In addition, we further propose a multi-region
convolution module, which emphasizes the central region of each patch. The
contextual information and central region features are modeled adaptively. The
experimental results on three SAR datasets demonstrate the effectiveness of the
proposed model. Our codes are available at
https://github.com/summitgao/SAR_CD_DDNet.
