Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland.
Faculty of Mathematics and Computer Science, University ofWarmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland.
Sensors (Basel). 2020 May 21;20(10):2919. doi: 10.3390/s20102919.
Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.
干涉合成孔径雷达（InSAR）数据经常受到射频干扰（RFI）伪影的污染，这使得处理它们变得更加具有挑战性。因此，易于实现的伪影识别技术有可能支持自动永久散射体干涉合成孔径雷达（PSInSAR）处理工作流程，在该流程中，错误的输入数据可能导致对最终结果的误解。为了解决这个问题，开发了一种有效的方法来标记具有 RFI 伪影的图像，并因此将其从 PSInSAR 处理工作流程中所需的合成孔径雷达（SAR）图像堆栈中删除，以计算地面位移。本文为了进行 RFI 检测而提出的技术基于图像处理方法，涉及像素卷积、阈值和最近邻结构滤波的特征提取。作为参考分类器，使用了卷积神经网络。
