An Unsupervised CNN-Based Multichannel Interferometric Phase Denoising Method Applied to TomoSAR Imaging
Jie Li, Zhongqiu Xu, Zhiyuan Li, Zhe Zhang, Bingchen Zhang, Yirong Wu
Abstract
Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3D spatial information. However, the standard deviation in the reconstructed elevation could be high due to the noise in the interferometric phases, which makes the denoising filter crucial before tomographic reconstruction. In this paper, we propose an unsupervised multichannel SAR interferometric phase denoising method based on the Convolution Neural Network (CNN). It utilizes the Weighted Least-Squares (WLS) regularization combining with the covariance of multichannel interferometric phases to minimize the standard deviation of phase noise, which leads to the accurate and complete TomoSAR reconstruction. This network is trained by real SAR images and the results of both simulated and real observations verify the effectiveness of our proposed method.