Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection
"> Figure 1
<p>Process of MSHPC acquisition. After testing all pixels in the window, the SHPCs, which have the maximum pixel number of SHPs, include MSHPC.</p> "> Figure 2
<p>Example of the double-layer hypothesis testing window. The red window is the multilook window, the yellow pixels are the SHPs of the multilook window and the green window is the spatial filtering window of a single SHP.</p> "> Figure 3
<p>Algorithm flow chart of ACDP-InSAR.</p> "> Figure 4
<p>(<b>a</b>) The location of Sentinel-1 data in China, and the black box represents range of Sentinel-1. (<b>b</b>) Geographic location of the coverage of the SAR acquisitions superposed on the TanDEM, and the blue box represents the range of Sentinel-1 data. (<b>c</b>) The optical image of study area, and (<b>d</b>) is the classification result of land used in the study area.</p> "> Figure 5
<p>(<b>a</b>) Magnitude image of the master image where the date is 20181012. (<b>b</b>) Average coherence coefficient of an interference pair. (<b>c</b>) CPs’ spatial distribution with a coherent threshold as 0.5. (<b>d</b>) CPs’ spatial distribution with a coherent threshold as 0.375. (<b>e</b>) CPs’ spatial distribution with a coherent threshold as 0.25. (<b>f</b>) DSPs’ spatial distribution. The white pixel (value as 1) represents the measuring point of CPs and DSPs in (<b>c</b>–<b>f</b>).</p> "> Figure 6
<p>(<b>a</b>–<b>c</b>) are the original phases of interference pairs 20191007–20191019, 20190913–20191019 and 20190209–20190317, respectively. (<b>d</b>−<b>f</b>) Phases 20191007–20191019, 20190913–20191019 and 20190209–20190317 after ACDP-InSAR processing.</p> "> Figure 7
<p>Time coherence of the study area in mountainous areas.</p> "> Figure 8
<p>(<b>a</b>) The surface deformation rate of the study area obtained by the CPT method. (<b>b</b>) The surface deformation rate of the study area obtained by the method proposed in this paper.</p> "> Figure 9
<p>(<b>a</b>) The location of Sentinel-1 data in China, and the black box represents range of Sentinel-1. (<b>b</b>) Geographic location of the coverage of the SAR acquisitions superposed on the TanDEM, and the blue box represents the range of Sentinel-1 data. (<b>c</b>) Optical image of the study area of Shigatse M5.9 earthquake in Tibet, China.</p> "> Figure 10
<p>(<b>a</b>) Magnitude image of the master image 20200108. (<b>b</b>) Average coherence coefficient of the interference pair.</p> "> Figure 11
<p>(<b>a</b>) The surface deformation rate of the study area in case 2 obtained by the CPT-In- SAR method. (<b>b</b>) The surface deformation rate of the study area in case 2 obtained by the method proposed in this paper.</p> "> Figure 12
<p>Cumulative processing time of the ACDP-InSAR and SqueenSAR over case 1 as an assessment of computational efficiency.</p> "> Figure 13
<p>The experimental area for ACDP-InSAR accuracy verification. (<b>a</b>) The location of the experimental area, and blue box represents the range of leveling data. (<b>b</b>) Distribution map of field observation points. Each benchmark in the figure has a corresponding label, and the color of each field observation point represents the annual average deformation rate obtain from leveling data. The corresponding color label is same as the color label in (<b>c</b>). The result of overlaying the level data deformation rate above the ACDP-InSAR deformation rate.</p> "> Figure 14
<p>Comparison between in situ leveling measurements and InSAR results estimated by the proposed method over the field observation points in <a href="#remotesensing-13-04784-f013" class="html-fig">Figure 13</a>b.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. CPT-InSAR Method
2.2. SHPCs Detection for DSP
2.3. The Phase Optimization of DSPs
2.4. The Process of ACDP-InSAR
3. Experiment
3.1. Case 1: Mountainous Areas in Southwestern China
3.1.1. Study Area and Dataset
3.1.2. Data Processing and Result
3.2. Case 2: Shigatse M5.9 earthquake in Tibet, China
3.2.1. Study Area and Dataset
3.2.2. Data Processing and Result
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Number | Classification System | Color | |
---|---|---|---|
1 | Impervious | (195,20,0) | |
2 | Evergreen broadleaved forest | (0,100,0) | |
3 | Shrubland | (150,100,0) | |
4 | Evergreen shrubland | (150,75,0) | |
5 | Rainfed cropland | (255,255,100) | |
6 | Closed deciduous broadleaved forest | (170,200,0) | |
7 | Irrigated cropland | (170,240,240) | |
8 | Water body | (0,70,200) |
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Dong, L.; Wang, C.; Tang, Y.; Zhang, H.; Xu, L. Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection. Remote Sens. 2021, 13, 4784. https://doi.org/10.3390/rs13234784
Dong L, Wang C, Tang Y, Zhang H, Xu L. Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection. Remote Sensing. 2021; 13(23):4784. https://doi.org/10.3390/rs13234784
Chicago/Turabian StyleDong, Longkai, Chao Wang, Yixian Tang, Hong Zhang, and Lu Xu. 2021. "Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection" Remote Sensing 13, no. 23: 4784. https://doi.org/10.3390/rs13234784