# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 3 Experiments and Results
### Results of the P-NG-Derived SGD Mask  
The parameters for the P-NG method were: (1) the iso-azimuth of the ascending and descending LOS of ALOS PALSAR for Chongqing; (2) the incidence angle (near range and far range), the angle the satellite illuminates the Earth's surface; and (3) the local terrain slope and aspect angles from the SRTM DEM data. These parameters were chosen to guarantee worldwide applicability of the proposed method.  
Figures 4 and 5 present the comparison of the SGD maps derived by the F-ALS and P-NG methods with respect to the ALOS ascending and descending images, respectively. Since the ALOS ascending and descending data were both recorded at a low incidence angle (around 38.7\({}^{\circ}\)), most of the areas were dominated by the resolution-enhancing effect. In addition, there were few shadow regions in both images. This indicates that the ALOS data is very suitable for landslide monitoring with InSAR.  
To provide more detailed results, a subregion, i.e., the black dashed boxes as shown in Figures 4 and 5, was selected to carry out the comparison. As shown in Figure 4e,f, the areas of layover and shadow obtained by the classical F-ALS method in the ascending data are obviously less than those obtained by the P-NG method that was in possession of the passive distortion areas. By contrast, the simulated layover gained by the P-NG method can highlight the characteristics of the layover on SAR images, i.e., \"many-to-one mapping\". Besides, it can be found that the range of the foreshortening areas (i.e., \"F\") in Figure 4f is significantly reduced compared with those in Figure 4e, where the reduced areas are occupied by passive distortion (i.e., \"PL\" and \"PS\"). Figure 5 shows the comparison of the SDG maps derived by the F-ALS and P-NG methods in the ALOS descending image, where similar results can be found.  
It is not difficult to find that there is a complementary relationship between the ascending and descending data for the simulated SGD in the same region. For instance, many layover distortions in the ascending data correspond to the shadow distortions in the descending data. Furthermore, the regions affected by the SGD in the ascending data are less than those in the descending data, indicating that the ascending data is more suitable for monitoring landslides in the study area.  
To further confirm the spatial interaction among the distortions proposed in this paper, we selected several regions which had not been corrected, as shown in Figure 6. The white points represent the passive layover, whereas the black points indicate the passive shadow. It can be noted that whether passive layover or passive shadow, they lie over the partial regions of the foreshortening, respectively. However, beyond that, passive layover interacts with parts of active and passive shadows, and so  
Figure 3: Shaded relief map of Chongqing. The red box indicates the scope of the study area. Blue and yellow dashed boxes indicate the coverage of the ascending and descending ALOS PALSAR datasets, respectively.  
does passive shadow. This phenomenon can be rectified by the P-NG method to obtain the SGD with accurate boundaries.  
Figure 4: Geometric distortions in the ALOS ascending image. (**a,d**) display the intensity image; (**b,e**) display the SDG map derived by the F-ALS method; (**c,f**) display the SDG map derived by the P-NG method. E: Resolution-Enhancing; F: Foreshortening; AS: Active Shadow; PS: Passive Shadow; AL: Active Layover; PL: Passive Layover.  
Figure 5: Geometric distortions in the ALOS descending image. (**a,d**) display the intensity image; (**b,e**) display the SDG map derived by the F-ALS method; (**c,f**) display the SDG map derived by the P-NG method. E: Resolution-Enhancing; F: Foreshortening; AS: Active Shadow; PS: Passive Shadow; AL: Active Layover; PL: Passive Layover.