Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network

TL;DR

This paper introduces a dual-domain neural network for SAR image change detection, integrating spatial and frequency domain features to improve noise robustness and focus on central patch regions, validated on three datasets.

Contribution

The novel dual-domain network incorporates DCT frequency features and a multi-region convolution module for enhanced SAR change detection.

Findings

Effective in reducing speckle noise

Improves detection accuracy on SAR datasets

Outperforms existing methods

Abstract

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.