SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

IF 0.9 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Yuqing Wu, Qing Xu, Zheng Zhang, Jingzhen Ma, Tianming Zhao, Xinming Zhu
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引用次数: 0

Abstract

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.
结合FFDNet空间去噪的SAR变化检测算法
目的:在对合成孔径雷达(SAR)图像进行变化检测时,差分图的质量对检测结果有重要影响,图像中的散斑噪声会干扰变化信息的提取。为了提高SAR图像变化检测的检测精度,提高差分图的质量,本文提出了一种将流行的深度神经网络与聚类算法相结合的方法。方法:首先构建带有散斑噪声的SAR图像,利用FFDNet架构对SAR图像进行再训练,获得对散斑噪声抑制效果较好的网络参数;然后利用网络输出的重构图像生成对数比算子。最后利用K-means和FCM聚类算法对差异图像进行分析,生成变化检测结果的二值图。结果:实验结果对Bern和Sulzberger的真实数据具有较高的检测精度,证明了该方法的有效性。
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来源期刊
CiteScore
2.30
自引率
25.00%
发文量
42
审稿时长
12-24 weeks
期刊介绍: The publishing of CARPATHIAN JOURNAL of EARTH and ENVIRONMENTAL SCIENCES has started in 2006. The regularity of this magazine is biannual. The magazine will publish scientific works, in international purposes, in different areas of research, such as : geology, geography, environmental sciences, the environmental pollution and protection, environmental chemistry and physic, environmental biodegradation, climatic exchanges, fighting against natural disasters, protected areas, soil degradation, water quality, water supplies, sustainable development.
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