WCMU-net: An Effective Method for Reducing the Impact of Speckle Noise in SAR Image Change Detection
As an inherent characteristic of synthetic aperture radar (SAR) systems, the presence of speckle noise reduces the signal-to-noise ratio (SNR) of SAR images, leading to blurred image details and limiting the accuracy of change detection in SAR images. For C-band SAR images, different satellite imagi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10783448/ |
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| Summary: | As an inherent characteristic of synthetic aperture radar (SAR) systems, the presence of speckle noise reduces the signal-to-noise ratio (SNR) of SAR images, leading to blurred image details and limiting the accuracy of change detection in SAR images. For C-band SAR images, different satellite imaging and ground object reflections can produce varying levels of noise, necessitating a network design adaptable to different noise levels. In this study, we introduce a focal loss function to enhance the robustness of the network, allowing for high-precision change detection of SAR images with different noise levels through parameter adjustment. To address the issue of low SNR in SAR images, we construct a deep learning network focused on feature exploration and utilization to effectively distinguish between noise and change information. The model initially employs discrete wavelet decomposition to extract high-frequency bands for noise reduction. Subsequently, conventional convolutions are utilized to extract high-level semantic information from frequency-domain images, with the resulting feature maps serving as patch inputs to the ConvMixer module to acquire blended spatial and positional information. In the decoder phase, valuable encoder features from multiscale attention gates are concatenated with corresponding upsampled features to select encoder features based on different resolutions adaptively. Through comparative experiments on real datasets, the superiority of the model is demonstrated. |
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| ISSN: | 1939-1404 2151-1535 |