*4.1. Experimental Data*

It is well-known that training a deep network requires at least a few hundred or more training datasets with labels. Of course, training the SMD-Net also needs enough interferograms with noise corresponding to the ideal interferograms. In order to obtain a large number of labeled training sets, we generated abundant noisy interferograms with corresponding ideal interferograms by utilizing a digital elevation model (DEM). This is helpful in enhancing the phase detail characteristic similarity between the simulated and measured interferograms [46,47]. The simulated interferometric phase can be obtained as follows: 

$$\mathfrak{op}\_{\mathfrak{C}} = \arg \left( e^{j2\pi \left( \mathbf{H}/h\_{\mathfrak{s}} \right)} \right) \tag{23}$$

where **H** is the height value of the DEM; arg(·) represents the complex argumen<sup>t</sup> operator; and *ha* indicates the ambiguity height, which was set to 92.13 m and was consistent with the measured InSAR data used in subsequent experiments.

According to the formulation of the interferometric phase in Section 2.1, the level of noise is related to the coherence coefficient *γ*, and the higher the coherence value, the less the noise in the InSAR phase data. Hence, interferograms with different coherence coefficients were simulated in order to enhance the generalization ability of the network. The coherence coefficients were in the range of [0.5, 0.95] and the interval was 0.05, which is helpful in that the network adapts to the noise level of a large number of real interferograms.

The generation manner of the simulated training sets is as follows. A simulated clean interferogram, shown in Figure 4b, is generated by employing the DEM (as illustrated in Figure 4a) of the eastern part of Turkey with the size of 2048 × 2048 from the SRTM 1Sec HGT based on Equation (23). Ten noise interferograms were generated by adding different levels of noise whose coherence coefficients were [0.5, 0.95] to the clean interferograms. Noisy interferograms with coherence coefficients of 0.5 and 0.95 are shown in Figure 4c,d. In this paper, we divided the whole interferogram into interferogram patches with the size 256 × 256 with a 0.5 overlap rate to obtain enough training sets and improve the computational efficiency. Among them, each noise interferogram with a coherence coefficient was cropped into a group containing 225 patches. Hence, the total number of interferogram patches with 2250 patches contained ten groups.

**Figure 4.** (**a**) The DEM used to generate the training sets; (**b**) clean interferogram generated by (**a**); (**c**) noisy interferogram with a coherence coefficient of 0.95; (**d**) noisy interferogram with a coherence coefficient of 0.5.

Figure 5a shows the DEM with the size of 1024 × 1024 from SRTM 1Sec HGT, which was used to generate the testing data illustrated in Figure 5b. Ten noise interferograms with different coherence coefficients were obtained by adding noise in the same way as the training data, among which the coherence coefficients of 0.5 and 0.95 are shown in Figure 5c,d. These testing interferograms with the different noise levels were also cut into 490 patches like the training data.

**Figure 5.** (**a**) The DEM used to generate the testing sets; (**b**) clean interferogram generated by (**a**); (**c**) noisy interferogram with a coherence coefficient of 0.95; (**d**) noisy interferogram with a coherence coefficient of 0.5.
