An Unsupervised CNN-Based Multichannel Interferometric Phase Denoising Method Applied to TomoSAR Imaging

Abstract

Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3-D 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 article, we propose an unsupervised multichannel SAR interferometric phase denoising method based on the convolution neural network. 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.

Keywords

• Convolution neural network (CNN)
• interfeometric phase
• SAR
• tomographic synthetic aperture radar (TomoSAR)