*4.2. Experimental Details*

In this article, we analyzed the performance of the SMD-Net on the experimental results of the simulated and measured data. The proposed method outperformed the previous three widely-used methods (i.e., the Lee filter [18], Goldstein filter [26], and InSAR-BM3D filter [28]) and the two methods based on DL (Phi-Net [31] and PFNet [30]). For a fair comparison, all experiments were carried out on an Inter® Core™ i7-2790K CPU with 4 Gb random access memory (RAM) and an NVIDIA GeForce GTX 980 Ti GPU, where the previous three widely-used methods were performed in MATLAB R2016b and the proposed method was tested in Pytorch.

Inspired by the work of [39], the SMD-Net contained nine blocks (i.e., nine iterations), each of which had the same network structure as follows: Γ1 is a convolution operator with 32 filters of the size 3 × 3; Γ2 is another convolution operator corresponding to 32 filters of the size 3 × 3 × 32. In our experiments, the SMD-Net was trained with the first 2000 interferograms of the training sets obtained in Section 4.1 by utilizing the Adam optimization [48] with a batch size of two. We set the initial learning rate, λ, α, and δ as 0.0001, 0.01, 0.2, and 0.1, respectively.
