# Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Differential Interferogram Phase of the SBAS-InSAR Technology

#### 2.2. Deep Convolution Neural Network

#### 2.3. Automatic Interferogram Selection Using the Proposed Method

#### 2.4. Establishment of Training Sets

## 3. Results and Discussions

#### 3.1. Simulation-Based Tests

#### 3.2. Actual Subsidence Issues

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Workflow of the proposed automatic interferogram selection for the SBAS-InSAR algorithm integrated with the DCNN method. (

**I**) The calculation of differential interferograms of sequential deformations from SAR images. (

**II**) The automatic extraction of high-quality interferograms by the ResNet50–DCNN model. (

**III**) The estimation of deformation field.

**Figure 5.**Histogram distribution for standard deviations of the interferogram phase based on interferograms selected by the ResNet50–DCNN method and those originally simulated.

**Figure 6.**The annual deformation rate based on (

**a**) the spatio–temporal baseline threshold method (

**b**) the manual method (

**c**) the ResNet50–DCNN method. (

**d**) Difference between (

**a**,

**b**). (

**e**) Histogram of (

**d**). (

**f**) Differences between (

**a**,

**c**). (

**g**) Histogram of (

**f**).

**Figure 10.**Interferograms selected by (

**a**) the spatio-temporal baseline threshold method, (

**b**) the manual method and (

**c**) the ResNet50–DCNN model method.

**Figure 11.**Distribution of the standard deviations of the interferogram phase based on the three different methods displayed in form of a histogram.

**Figure 12.**Vertical deformation rates based on (

**a**) the spatio–temporal baseline threshold method (

**b**) the manual method (

**c**) the ResNet50-DCNN method. (

**d**) Distribution of the main subsidence areas (C1–C6) and the permanent scatter points (PS). Points A–G: Time series of specific PS points (see also Figure 13).

**Figure 13.**Time series of cumulative deformations of 7 PS targets ((

**a**–

**g**) marked in Figure 12d) based on the three investigated methods.

**Figure 14.**Annual average deformation rate of 30 randomly selected PS points obtained by the three investigated methods. See also Figure 12d.

Layer Name | Output Size | Configuration |
---|---|---|

CONV_1 | 1/2 | 7$\times $7, 64, stride = 2 |

CONV_2 | 1/4 | $\left[\begin{array}{cc}1\times 1,& 64\\ 3\times 3,& 64\\ 1\times 1,& 256\end{array}\right]\times 3$ |

CONV_3 | 1/8 | $\left[\begin{array}{cc}1\times 1,& 128\\ 3\times 3,& 128\\ 1\times 1,& 512\end{array}\right]\times 4$ |

CONV_4 | 1/16 | $\left[\begin{array}{cc}1\times 1,& 256\\ 3\times 3,& 256\\ 1\times 1,& 1024\end{array}\right]\times 6$ |

CONV_5 | 1/32 | $\left[\begin{array}{cc}1\times 1,& 512\\ 3\times 3,& 512\\ 1\times 1,& 2048\end{array}\right]\times 3$ |

Classifier | 1$\times $1 | Average pooling, Fc (Full connection), 1000, Softmax |

Size of Input Image | Training Cycles | Interferogram Number | Standard Deviation of Interferogram Phase/Rad | Accuracy (%) |
---|---|---|---|---|

128 × 128 | 100 | 432 | 1.699 | 74.4 |

128 × 128 | 200 | 447 | 1.6829 | 86.34 |

128 × 128 | 300 | 425 | 1.6406 | 88.53 |

128 × 128 | 400 | 461 | 1.7302 | 81.28 |

128 × 128 | 500 | 431 | 1.7005 | 80.10 |

128 × 128 | 600 | 441 | 1.6749 | 86.00 |

128 × 128 | 700 | 464 | 1.7118 | 87.18 |

128 × 128 | 800 | 456 | 1.6982 | 85.50 |

32 × 32 | 300 | 437 | 1.7537 | 71.0 |

64 × 64 | 300 | 448 | 1.7153 | 75.71 |

128 × 128 | 300 | 425 | 1.6406 | 88.53 |

256 × 256 | 300 | 414 | 1.6225 | 89.00 |

512 × 512 | 300 | 411 | 1.6585 | 81.62 |

**Table 3.**Derived number and standard deviations of the interferogram phase obtained by the three methods.

The Spatio–Temporal Baseline Threshold Method | The Manual Method | The ResNet50–DCNN Method | |
---|---|---|---|

Number of interferogram | 593 | 411 | 425 |

Standard deviation of interferogram phase/rad | 2.1054 | 1.6328 | 1.6406 |

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**MDPI and ACS Style**

He, Y.; Zhang, G.; Kaufmann, H.; Xu, G.
Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks. *Remote Sens.* **2021**, *13*, 4468.
https://doi.org/10.3390/rs13214468

**AMA Style**

He Y, Zhang G, Kaufmann H, Xu G.
Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks. *Remote Sensing*. 2021; 13(21):4468.
https://doi.org/10.3390/rs13214468

**Chicago/Turabian Style**

He, Yufang, Guangzong Zhang, Hermann Kaufmann, and Guochang Xu.
2021. "Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks" *Remote Sensing* 13, no. 21: 4468.
https://doi.org/10.3390/rs13214468