SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

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.

基于改进FFDNet的海洋溢油检测方法研究

海洋溢油检测是重要的海洋观测任务之一,对人类社会发展有重要的意义。提出了一种针对SAR遥感影像的海洋溢油检测方法,通过改进FFDNet实现了相干斑噪声的去除任务,并通过自适应参数完成了SAR影像中的溢油区域的提取。另外,还提出了配套的在线溢油检测系统的软件架构,该软件具有较好的鲁棒性和易扩展性。所提出的方法在一定程度上改善了传统阈值分割或机器学习算法中高度依赖阈值设定或者模型超参数设置、存在较大主观性和不确定性、检测精度不高、泛化能力弱的问题。