An Atmospheric Phase Correction Method Based on Normal Vector Clustering Partition in Complicated Conditions for GB-SAR
Abstract
:1. Introduction
2. Related Works
2.1. Methods Based on Meteorological Data
2.2. Methods Based on Permanent Scatterers
2.2.1. Permanent Scatterers
2.2.2. Conventional Model-Based Methods
2.2.3. Some Novel Data-Driven Methods
3. Data Acquisition and Analysis
3.1. Data Acquisition
3.2. Data Analysis
3.2.1. PSs Selection
3.2.2. Spatial Phase Wrapping in the Dataset
3.2.3. Cumulative Phase Analysis
4. Methodology
4.1. Algorithm Flowchart
4.2. Data Pre-Processing
- (1)
- Construct a Delaunay triangulation network, which is denoted T, to connect all PS points. The IDWI is performed only within the convex packet of PSs. Delaunay triangulation is an optimized spatial structure and can make the interpolation result automatically approach the regular triangle, improving the interpolation precision [28,29];
- (2)
- For a pending interpolation point p, in the convex package, it must be inside a triangle T, and three vertices of this triangle , , and are selected as reference points for interpolation;
- (3)
- The estimated phase of p, which is denoted , is calculated by
4.3. Spatial Normal Vector Estimation
- (1)
- Perform a k-nearest neighbor search, to find the nearest k CPSs to p, the points set is denoted N:The general form of the plane to be fitted is:
- (2)
- Construct the covariance matrix for N:
- (3)
- Solve the normalized eigenvectors corresponding to the minimum eigenvalue of , to obtain the normal vector:
- (4)
- Adjust the direction of so that , to ensure that all normal vectors point to the same side.
4.4. Clustering Partition
- (1)
- Construct the SCNV set , where m is the number of all CPS points;
- (2)
- Set the number of clusters to and initialize the clustering center ;
- (3)
- Calculate the Euclidean distance from to each cluster center and assign to the cluster with the closest Euclidean distance;
- (4)
- Calculate the center of mass of each cluster and update the cluster center with the center of mass;
- (5)
- The above steps are iteratively processed until the clustering centers no longer change, or a predetermined number of iterations is reached.
- (1)
- Calculate the average normal vector of each too-small sub-block;
- (2)
- Search the sub-blocks adjacent to the too-small sub-block, calculate their average normal vectors, and then calculate the spatial distance between these average normal vectors and the normal vector of the too-small sub-block;
- (3)
- Merge the too-small sub-block into the block corresponding to the minimum spatial distance in step 2.
4.5. Atmospheric Phase Correction
5. Results and Analysis
5.1. APS Estimation on Interferograms
5.2. Simulation Experiment of Deformation Monitoring
5.3. Parameter Settings Analysis
5.3.1. Effect of and on Atmospheric Phase Correction Accuracy
5.3.2. Effect of and on Deformation Retention Rate
5.3.3. Effect of
5.4. Time Series Atmospheric Phase Correction
6. Discussions
6.1. Complicated Distribution of Atmospheric Phase
- (1)
- High altitude. The altitude of the Dabao Mountain is between 600 and 800 m. The solar radiation is stronger than that in lower altitude areas, and the air is relatively thin and poorly insulated. This also leads to large changes in atmospheric parameters within a short period of time, and therefore significant diurnal variations. During the daytime, the monitored area is mostly in clear or cloudy weather, and there was no obvious rainfall process during the data acquisition, so the influence of solar irradiation on temperature and humidity is significant. The heat brought by the sun makes the atmospheric parameters change significantly, so the distribution of the AP is more dispersed, and the spatial phase wrapping phenomenon can easily occur. After the rapid loss of heat at night, the change in atmospheric parameters tends to be smooth, and the AP becomes smaller and more stable accordingly.
- (2)
- Steep terrain of the mine. The Dabao Mountain Mine has been mined for many years and the mountain is very steep. The relative elevation from the bottom of the pit to the top of the mine is about 150 m. The difference in elevation makes the spatial distribution of atmospheric parameters non-uniform, resulting in significant changes in the spatial distribution of AP with the change in elevation.
6.2. Comparison of the Conventional Methods and the Proposed Method
6.3. Conflict between Accuracy and Credibility
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameters | Value |
---|---|
Carrier frequency | 24 GHz |
Beam coverage | |
Range resolution | m |
Angle resolution | 6 mrad |
Detection range | 4000 m |
Parameters | Value |
---|---|
50 | |
10 | |
100 |
Uncorrected | 2nd-Order Slant Distance Model | 2nd-Order Slant Distance Model with Azimuthal Partition | Slant Distance & Azimuth Model | Proposed Method | |
---|---|---|---|---|---|
Mean Value | 0.2310 | 0.2071 | 0.1601 | 0.1897 | 0.1018 |
Median Value | 0.1766 | 0.1631 | 0.1285 | 0.1523 | 0.0872 |
2nd-Order Slant Distance Model | 2nd-Order Slant Distance Model with Azimuthal Partition | Slant Distance & Azimuth Model | Proposed Method with Overfitting Correction | |
---|---|---|---|---|
A | 0.1818 | 0.5090 | 0.0910 | 0.0364 |
B | 0.2170 | 0.1022 | 0.0941 | 0.0469 |
C | 0.0969 | 0.0786 | 0.1791 | 0.0578 |
D | 0.2567 | 0.0629 | 0.1278 | 0.0300 |
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Ou, P.; Lai, T.; Huang, S.; Chen, W.; Weng, D. An Atmospheric Phase Correction Method Based on Normal Vector Clustering Partition in Complicated Conditions for GB-SAR. Remote Sens. 2023, 15, 1744. https://doi.org/10.3390/rs15071744
Ou P, Lai T, Huang S, Chen W, Weng D. An Atmospheric Phase Correction Method Based on Normal Vector Clustering Partition in Complicated Conditions for GB-SAR. Remote Sensing. 2023; 15(7):1744. https://doi.org/10.3390/rs15071744
Chicago/Turabian StyleOu, Pengfei, Tao Lai, Shisheng Huang, Wu Chen, and Duojie Weng. 2023. "An Atmospheric Phase Correction Method Based on Normal Vector Clustering Partition in Complicated Conditions for GB-SAR" Remote Sensing 15, no. 7: 1744. https://doi.org/10.3390/rs15071744