## 5 Results and Analysis
Following 3D positioning, refined baselines were utilized to generate a DSM, which was then geocoded to obtain the geographically referenced RAW DSM, as depicted in Figure 8.  
Figure 9 presents the fused RAW DSM product utilizing elevation ambiguity. The fused DSM effectively fills the voids in shadowed and overlapping areas, resulting in a notable enhancement in DSM quality. Finally, the accuracy of the DSM was validated by comparing it with ICESat-2 global geolocated photon data and TanDEM DSM data. There were a total of 1030 ICESat-2 checkpoints on the imagery, with a 0.06 m ME and a 0.96 m RMSE  
Figure 8: Digital surface model (DSM) from each 2 satellite data. (A-B represents the DSM between PIESAT-01A and PIESAT-01B, A-C represents the DSM between PIESAT-01A and PIESAT-01C, A-D represents the DSM between PIESAT-01B and PIESAT-01C, B-D represents the DSM between PIESAT-01B and PIESAT-01D, C-D represents the DSM between PIESAT-01C and PIESAT-01D). Figure 9: Fused digital surface model (DSM) of PIESAT-01 constellation. Additionally, a histogram of the differences between the PIESAT-01 DSM and the TanDEM DSM, as shown in Figure 10, confirmed the phase coherence of the imaging algorithm and the interferometric measurement and 3D reconstruction capabilities of the data. To further validate the imaging performance and DSM product quality in various terrain scenarios, three areas with distinct terrains were selected for DSM generation and accuracy assessment. Figure 11 shows the location of the selected research area. Dingxi is located in the northwestern region of China and is characterized by typical mountainous terrain. Figure 12 is the optical imagery of the Qinghai research area on Google Earth. Complex mountainous regions are the best areas for testing the advantages of multi-baseline DSM production. Fewer baselines often result in areas with numerous phase unwrapping errors, which are then masked out, leading to discontinuities in the DSM results. Therefore, we selected this area to validate the interferometric capabilities of the PIESAT-01 constellation in mountainous regions. Figure 13 shows the RAW DSM of a  
Figure 11: Selected research area location map. Figure 10: PIESAT-01 DSM and TanDEM DSM difference histogram. specific area in Dingxi generated based on PIESAT-01 data. The texture of the mountainous area is clear, and rivers are distinctly visible, with no discontinuities caused by phase unwrapping errors due to phase jumps. This effectively demonstrates the fine details in the data. A total of 28 ICESat-2 checkpoints were identified on the imagery, with a 0.47 m ME and a 2.06 m RMSE for the altimetry points. Additionally, a histogram of the differences between the PIESAT-01 DSM and the TanDEM DSM is illustrated in Figure 5:0  
Figure 5:1 Dingxi research area optical image on Google Earth. The white box represents the scope of the research area. Figure 5:2 PIESAT-01 DSM of Dingxi Region. Mali, situated in the central-western region of West Africa, features the Sahara Desert in its northern part. Figure 15 is the optical imagery of the Mali research area on Google Earth. Desert regions are ideal for testing imaging and registration algorithms. Incorrect imaging can lead to registration failures, thereby preventing DSM production. Therefore, we have chosen desert areas as another validation zone. Figure 16 depicts the RAW DSM of the desert region in Mali generated based on PIESAT-01 data, illustrating clear textures and orientations of sand dunes. A total of 1555 ICESat-2 checkpoints were identified on the imagery, with a mean error of 0.27 m and a standard deviation of 0.67 m for the altimetry points. Additionally, a histogram illustrating the differences between the PIESAT-01 DSM and the TanDEM DSM is shown in Figure 5:3  
Figure 5:4 PIESAT-01 DSM and TanDEM DSM difference histogram of Dingxi Region. Figure 5:5 Mali research area optical image on Google Earth. The white box represents the scope of the research area.