**3. Methodology**

According to the phase filtering model established in Section 2.2, which aimed at performing a fast ye<sup>t</sup> accurate filtering method, we propose a sparse-model-driven network (SMD-Net) for efficient and high-accuracy InSAR phase filtering by combining the interpretability and fewer parameters of SR and the speed advantage of the CNN. Inspired by the idea of the unrolled SR algorithms, the designed network casts the phase filtering model into the unrolled network and implements the complex operation of the unrolled network. In the filtering process shown in Figure 1, first of all, the SMD-Net is trained by employing the real and imaginary parts of the training datasets obtained by Equation (12). Then, the real and imaginary parts of the noisy interferograms (testing data) are entered into the trained SMD-Net, and the filtered real and imaginary parts corresponding to the input are recombined into the estimated interferometric phase using Equation (14). Finally, the filtered phase patches are spliced together by using an image fusion algorithm. Next, we will introduce this section in detail from the design of the SMD-Net and the loss function.

**Figure 1.** The flow chart of the interferometric phase filtering via the SMD-Net.
