Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network
This work addresses change detection in SAR imagery, a critical task for remote sensing applications, by introducing a novel dual-domain approach that is incremental over existing spatial-focused methods.
The paper tackled change detection in synthetic aperture radar images by proposing a Dual-Domain Network that integrates frequency domain features via discrete cosine transform and uses a multi-region convolution module to reduce noise and emphasize central patch regions, demonstrating effectiveness on three SAR datasets.
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.