SAR image edge detection: review and benchmark experiments

Meester, M. J.; Baslamisli, Anil S.

doi:10.1080/01431161.2022.2131480

Cited by 4 publications

(1 citation statement)

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“…The existing CNN denoising tends to Gaussian noise, and has poor generalization ability for real noisy images with more complex noise [16] . In order to solve the problem of different noise levels, the topic of edge detection on SAR images has been extensively studied in the literature [17] , providing valuable knowledge to improve our understanding and application of Synthetic Aperture Radar (SAR) technology. Kai et al [14] used noise level map as input.…”

Section: Ffdnet Denoising Modelmentioning

confidence: 99%

SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

Wu,

Xu,

Zhang

et al. 2023

j. of environ. & earth. sci.

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Objectives: When detecting changes in synthetic aperture radar (SAR) images, the quality of the difference map has an important impact on the detection results, and the speckle noise in the image interferes with the extraction of change information. In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map, this paper proposes a method that combines the popular deep neural network with the clustering algorithm. Methods: Firstly, the SAR image with speckle noise was constructed, and the FFDNet architecture was used to retrain the SAR image, and the network parameters with better effect on speckle noise suppression were obtained. Then the log ratio operator is generated by using the reconstructed image output from the network. Finally, K-means and FCM clustering algorithms are used to analyze the difference images, and the binary map of change detection results is generated. Results: The experimental results have high detection accuracy on Bern and Sulzberger's real data, which proves the effectiveness of the method.

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Section: Ffdnet Denoising Modelmentioning

confidence: 99%