# Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Polarimetric SAR Interferometry (PolInSAR)

#### 2.2. Polarimetric Persistent Scatterer Interferometry with Amplitude Dispersion Index Optimization (PolPSI-ADI)

#### 2.3. Polarimetric Persistent Scatterer Interferometry with Coherence Optimization (PolPSI-COH)

#### 2.4. Polarimetric Persistent Scatterer Interferometry with the Adaptive Optimization Strategy (PolPSI-AOS)

#### 2.4.1. Coherency Matrix Filtering

#### 2.4.2. Polarimetric Optimization

## 3. Data Sets and Test Sites

## 4. Results and Analysis

#### 4.1. Results of the PolPSI-ADI

#### 4.1.1. ${D}_{A}$ Optimization Results

#### 4.1.2. Performance on Interferograms’ Optimizations

#### 4.1.3. Ground Deformation Estimation

#### 4.2. Results of the PolPSI-COH

#### 4.2.1. Coherence and Interferograms’ Optimization Results

#### 4.2.2. Ground Deformation Estimation

#### 4.3. Results of the PolPSI-AOS

#### 4.3.1. Performance on Interferograms’ Optimizations

#### 4.3.2. Ground Deformation Estimation

## 5. Discussion

## 6. Conclusions

- (1)
- All the three types of PolPSI techniques are able to improve interferograms’ phase qualities through the polarimetric optimization with VV and VH Sentinel-1 images. After the polarimetric optimizations, edges of structures become more clear and phase noises are reduced.
- (2)
- The improvement in density of final deformation monitoring pixels with respect to conventional PSI techniques is $50\%$, $12\%$, and $348\%$ for PolPSI-ADI, PolPSI-COH, and PolPSI-AOS, respectively. The PolPSI-AOS algorithm is with the best performance among the three, which also has the longest computation time.
- (3)
- PolPSI-ADI is the most efficient (fast) algorithm, and it is the first choice when applying to the areas with abundant PS pixels. PolPSI-COH is not suggested to be applied on Sentinel-1 PolSAR images, because it has small improvement and relatively long computation time with respect to conventional PSI method as the results indicate. PolPSI-AOS is suggested to be applied for areas where DS pixels have to be employed to retrieve ground deformation with Sentinel-1 PolSAR images.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Flowchart of the Polarimetric Persistent Scatterer Interferometry with the Adaptive Optimization Strategy (PolPSI-AOS). The abbreviation PHPs, NPHPs, PSs, DSs, MMSE filter, and PolOPT represents the polarimetric homogeneous pixels, the number of polarimetric homogeneous pixels, persistent scatterers, distributed scatterers, the minimum mean square error filter, and the polarimetric optimization, respectively.

**Figure 2.**(

**a**) Locations of the three test sites. (

**b**–

**d**) show the SAR images’ coverage (indicated by the red rectangle) and the GoogleEarth optical image over the Beijing, Fukang, and XiaoLangDi test site, respectively.

**Figure 3.**The temporal and perpendicular baseline of the generated interferogram stack over the (

**a**) Beijing, (

**b**) Fukang, and (

**c**) XiaoLangDi test site, respectively. The filled diamonds and the crosses represent reference (primary) and secondary SAR images forming the interferograms, respectively.

**Figure 4.**(

**a**) Amplitude dispersion index (${D}_{A}$) histograms derived by the VV channel and PolPSI-ADI approach (VV+VH) over Beijing test site. (

**b**) is the detailed zoom of (

**a**) for ${D}_{A}$ values from 0 to 0.4.

**Figure 5.**Interferograms of VV channel (

**a**,

**d**,

**g**,

**j**) and the corresponding optimized ones via PolPSI-ADI approach (

**b**,

**e**,

**h**,

**k**) of two subsections over Beijing test site. (

**c**,

**f**,

**i**,

**l**) are the corresponding residual phase maps (i.e., PolPSI-ADI phase minus PSI phase). (

**a**–

**f**) are over the first sub-area, while (

**g**–

**l**) are over the other one, and (

**m**,

**n**) are the corresponding average amplitude images of these two subsections. The number on the left of each row indicates the interferograms and residual phase maps related temporal baselines, all the subfigures are in SAR coordinate.

**Figure 6.**Ground deformation velocity (in the line-of-sight direction) estimated by the (

**a**) PSI and (

**b**) PolPSI-ADI algorithms, respectively. The numbers in (

**a**,

**b**) represent respectively the final numbers of PS pixels obtained by the two approaches, and the improvement percentage in the bracket in (

**b**) is calculated by taking the PSI method as a reference. (

**c**,

**e**) are the PSI derived results over the two subareas limited by the rectangle A and B in (

**a**). (

**d**,

**f**) are the counterparts of (

**c**,

**e**) derived by the PolPSI-ADI algorithm.

**Figure 7.**(

**a**–

**c**) are the PSI derived ground deformation (in the line-of-sight direction) over the three subareas that are highlighted in Figure 6c,e with white rectangles C, D, and E, respectively. (

**d**–

**f**) are the counterparts of (

**a**–

**c**) derived by the PolPSI-ADI algorithm.

**Figure 8.**(

**a**–

**c**) shows the ground deformation time-series retrieved by PSI and PolPSI-ADI of the three selected pixels P1, P2, and P3 over Beijing test sites, respectively. The locations of P1, P2, and P3 are highlighted by the three white circles in Figure 6a.

**Figure 9.**(

**a**,

**b**) shows the temporal average coherence of VV channel and the PolPSI-COH approach, respectively.

**Figure 10.**Interferograms of VV channel (

**a**,

**d**), Boxcar (

**b**,

**e**,

**h**,

**j**), and PolPSI-COH (

**c**,

**f**,

**i**,

**k**) over the a subsection of Fukang area, respectively. The number on the left of the first and second row indicates the corresponding interferograms’ temporal baseline. (

**g**) is the corresponding SAR amplitude image, (

**h**,

**j**) shows the close-up area (limited by the black rectangle in (

**b**,

**e**)) in (

**b**,

**e**), respectively. The PolPSI-COH counterparts over the same close-up area are shown in (

**i**,

**k**). All the sub-figures are in SAR coordinate.

**Figure 11.**(

**a**,

**b**) shows the deformation velocity (in the line-of-sight direction) derived by the PSI (VV) and PSI-COH (VV+VH) approach, respectively. The number represents the final number of selected pixels for each approach, and the improvement percentage in the brackets in (

**b**) is with respect to those derived by the PSI approach. (

**c**) is the additional pixels (i.e., the pixel density improvement) achieved by PolPSI-COH with respect to that of PSI. (

**d**,

**e**) shows the detailed ground deformation obtained by PSI and PolPSI-COH over the Laobata coal fire area (black rectangle in (

**a**) highlighted), respectively. (

**f**) is the additional pixels (i.e., the pixel density improvement) achieved by PolPSI-COH with respect to that of PSI over the Laobata coal fire area. The red dots indicate the locations of filed survey detected coal fire points, and the white circle in (

**d**) highlights the location of the selected pixels for deformation time-series analysis in Figure 12.

**Figure 12.**The deformation time-series of a selected pixel P1 within the Laobata coal fire area. The location of P1 is highlighted by the white circle in Figure 11d.

**Figure 13.**Interferograms of VV channel (

**a**,

**d**), MMSE (

**b**,

**e**), and PolPSI-AOS (

**c**,

**f**) over the XiaoLangDi dam, respectively. The number on the left of the first and second row indicates the corresponding interferograms’ temporal baseline. (

**g**) is the optical image over the dam, which is rotated for better visualization. (

**h**,

**i**) shows the number of identified PHPs of each pixel and the SAR amplitude image over this subarea, respectively. Except the optical image, which is in geographic coordinate, the other sub-figures are in SAR coordinate.

**Figure 14.**(

**a**–

**c**) shows the deformation velocity (in the line-of-sight direction) over XiaoLangDi area derived by PSI (VV), MMSE (VV), and PolPSI-AOS (VV+VH) approach, respectively. The number represents the final number of selected pixels for each approach, and the improvement percentage in the brackets in (

**b**,

**c**) is with respect to those derived by the PSI approach. (

**d**–

**f**) are a detailed zoom of the white rectangle indicated area (i.e., the dam) in (

**a**–

**c**).

**Figure 15.**The selected pixels along with a profile (from A to A’) over the XiaoLangDi obtained by the (

**a**) PSI, (

**b**) MMSE, and (

**c**) PolPSI-AOS, respectively. The location of the profile is shown in (

**d**) by the white line over the PolPSI-AOS retrieved displacements. The three filled dots in (

**d**) indicate locations of the pixels selected for time-series deformation analysis in Figure 16. It is worth to note that (

**d**) is in UTM (Universal Transverse Mercator) coordinate.

**Figure 16.**The three methods obtained time series deformations of the three selected pixels (i.e., P1, P2, and P3), which are along with the profile over the dam front in Figure 15d. The reservoir basin water level of the same observing duration is also depicted for the analysis of the dam deformation phenomena.

Acquisition Mode | IW | ||
---|---|---|---|

Polarization | VV + VH | ||

Resolution | 5 × 20 m | ||

Wavelength | 5.55 cm | ||

Orbit | Ascending | ||

Test Sites | Beijing | Fukang | XiaoLangDi |

NO. of SAR images | 46 | 40 | 38 |

Reference SAR images | 20181111 | 20170922 | 20171009 |

NO. of intferograms | 45 | 39 | 37 |

NO. of pixels | $5300\times 16,500$ | $1100\times 4000$ | $1100\times 4000$ |

**Table 2.**Statistical analysis of the PSI and PolPSI-ADI retrieved ground deformation results over Beijing.

D. V. | PSI P. N. | PolPSI-ADI P. N. | P. N. Impro. | Add. Area |
---|---|---|---|---|

20 to 10 (mm/yr) | 65,376 | 103,633 | 38,257 (58.52%) | 382.57 hm${}^{2}$ |

10 to 0 (mm/yr) | 646,977 | 950,855 | 303,878 (46.97%) | 3038.78 hm${}^{2}$ |

0 to −10 (mm/yr) | 915,308 | 1,371,704 | 456,396 (49.86%) | 4563.96 hm${}^{2}$ |

−10 to −20 (mm/yr) | 214,169 | 327,669 | 113,500 (53.00%) | 1135.00 hm${}^{2}$ |

−20 to −40 (mm/yr) | 124,957 | 202,366 | 77,409 (61.95%) | 774.09 hm${}^{2}$ |

−40 to −60 (mm/yr) | 15,990 | 21,122 | 5132 (32.10%) | 51.32 hm${}^{2}$ |

−60 to −80 (mm/yr) | 2944 | 4264 | 1320 (44.84%) | 13.20 hm${}^{2}$ |

**Table 3.**Statistical analysis of PSI and PolPSI-COH obtained ground deformation results over Fukang area.

D. V. | PSI P. N. | PolPSI-COH P. N. | P. N. Impro. | Add. Area |
---|---|---|---|---|

10 to 0 (mm/yr) | 344,163 | 385,999 | 41,836 (12.16%) | 418.36 hm${}^{2}$ |

0 to −10 (mm/yr) | 323,173 | 360,248 | 37,075 (11.47%) | 370.75 hm${}^{2}$ |

−10 to −15 (mm/yr) | 827 | 1023 | 196 (23.70%) | 1.96 hm${}^{2}$ |

**Table 4.**Statistical analysis of PSI and PolPSI-AOS obtained ground deformation results over XiaoLangDi area.

D. V. | PSI P. N. | PolPSI-AOS P. N. | P. N. Impro. | Add. Area |
---|---|---|---|---|

10 to 0 (mm/yr) | 10,124 | 41,956 | 31,832 (314.42%) | 318.32 hm${}^{2}$ |

0 to −10 (mm/yr) | 4593 | 25,491 | 20,898 (455.00%) | 208.98 hm${}^{2}$ |

−10 to −20 (mm/yr) | 739 | 1656 | 917 (124.09%) | 9.17 hm${}^{2}$ |

Method | Time (M. T.) | Improvement | Test Site |
---|---|---|---|

PolPSI-ADI | 100 h (1.1 h) | 50% | Beijing (5300 × 16,500) |

PolPSI-COH | 46 h (10.5 h) | 12% | Fukang (1100 × 4000) |

PolPSI-AOS | 45 h (11.3 h) | 348% | XiaoLangDi (1000 × 4000) |

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**MDPI and ACS Style**

Zhao, F.; Wang, T.; Zhang, L.; Feng, H.; Yan, S.; Fan, H.; Xu, D.; Wang, Y.
Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data. *Remote Sens.* **2022**, *14*, 309.
https://doi.org/10.3390/rs14020309

**AMA Style**

Zhao F, Wang T, Zhang L, Feng H, Yan S, Fan H, Xu D, Wang Y.
Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data. *Remote Sensing*. 2022; 14(2):309.
https://doi.org/10.3390/rs14020309

**Chicago/Turabian Style**

Zhao, Feng, Teng Wang, Leixin Zhang, Han Feng, Shiyong Yan, Hongdong Fan, Dongbiao Xu, and Yunjia Wang.
2022. "Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data" *Remote Sensing* 14, no. 2: 309.
https://doi.org/10.3390/rs14020309