Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data
Abstract
:1. Introduction
2. TomoSAR Imaging Based on Compressive Sensing
2.1. TomoSAR System Mode
2.2. Compressive Sensing
3. Materials and Methods
3.1. Joint Sparsity Basic
Algorithm 1: Procedure to Extract LOI, Mask, and Iso-Height Lines from Building POI. |
1: #Generate the LOI; 2: Import image and identify the POI of the test building by geodetic surveying; 3: Connect the POI facing the SAR sensor side to form the LOI and transform it to SAR coordinate system by geocoding; 4: Initialize the max-shift range of the surveyed area and find the pixels that LOI passed; 5: #Generate Mask; 6: While “range shift ≤ range limit”, one must: 7: Shift the LOI in the range direction by a distance of 1 and find the pixels passed in every shift; 8: Compute the pixel average intensity value of every shift; 9: Compute the intensity difference value between every pixel shift and the LOI; 10: Find the maximum difference value; 11: Otherwise, break; 12: #Generate iso-height lines; 13: While “range shift ≤ range limit of Mask”, one must; 14: Shift the LOI only in the range direction by the sub-pixel distance; 15: Compute the distance between a pixel and its adjacent iso-height lines and find the closest iso-height line to the pixel; 16: The pixels belonging to the same iso-height line are associated; 17: Construct new sparse scenario; 18: Otherwise, break; 19: # BIC-MLE; 20: Initialize K == 0, while K == 0–4; 21: Calculate each model based on the BIC for every pixel; 22: Find the model that best fits each pixel, and estimate the elevation, amplitude, and phase; 23: K = K + 1; 24: Otherwise, end. |
3.2. Building LOI
3.3. Building Mask
3.4. Building ISO-Height Lines
4. Verification of Simulation Data
5. Practical Test Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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POI | Latitude/Deg | Longitude/Deg | Height/M |
---|---|---|---|
A | 22.55513197 | 113.88121665 | 0.987 |
B | 22.55513975 | 113.88109875 | 1.012 |
C | 22.55564238 | 113.88098455 | 0.995 |
RMSE | Joint Sparsity | OMP |
---|---|---|
N = 18 | 0.554 | 0.919 |
N = 10 | 1.097 | 1.679 |
Mean Value | Joint Sparsity | OMP |
---|---|---|
D = 20 | 0.012 0.017 | 0.018 0.024 |
D = 10 | 0.013 0.019 | 0.028 0.129 |
D = 05 | 0.022 0.025 | 0.139 0.279 |
Some Parameters of a TerraSAR-X Staring Spotlight Acquisition of Shenzhen | |
---|---|
Incident Angle | 35.380° |
Polarization Mode | HH |
Number of Azimuth Beams | 113 |
Azimuth Steering Angle | ±2.210° |
Azimuth Resolution | 0.230 m |
Slant Range Resolution | 0.588 m |
Scene Azimuth Extent | 3052.988 m |
Scene Range Extent | 6262.264 m |
Common PRF | 42,400 Hz |
Azimuth Look Bandwidth | 38,292.780 Hz |
Range Look Bandwidth | 300 MHz |
Scene Duration Time | 0.43 s |
RAW Duration Time | 6.73 s |
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Pang, L.; Gai, Y.; Zhang, T. Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data. Sensors 2021, 21, 6888. https://doi.org/10.3390/s21206888
Pang L, Gai Y, Zhang T. Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data. Sensors. 2021; 21(20):6888. https://doi.org/10.3390/s21206888
Chicago/Turabian StylePang, Lei, Yanfeng Gai, and Tian Zhang. 2021. "Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data" Sensors 21, no. 20: 6888. https://doi.org/10.3390/s21206888
APA StylePang, L., Gai, Y., & Zhang, T. (2021). Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data. Sensors, 21(20), 6888. https://doi.org/10.3390/s21206888