Deformation-Monitoring-Dev

The source code of Paper: Deep learning for localized rapid deformation detection and InSAR phase unwrapping

Authors: Zhipeng Wu, Student Member, IEEE, Teng Wang, Yingjie Wang, Robert Wang, Senior Member, IEEE, Daqing Ge

Introduction

This is the source code for training and testing PUNet/DDNet, implemented in the PyTorch framework version 1.8.0 based on Python 3.6.

For code to generate the training dataset, see InterferogramSimulator.

Installation

Assume you have Python 3.6 installed.

1. Clone the repo:

   git clone https://github.com/Wu-Patrick/Deformation-Monitoring-Dev.git
   cd Deformation-Monitoring-Dev

2. Install dependencies:

   pip install -r requirements.txt

Training

1. Input arguments: (see full input arguments via python train.py --help):

usage: train.py [-h] [--model MODEL] [--dataRootDir DATAROOTDIR]
                [--dataset DATASET] [--input_size INPUT_SIZE]
                [--num_workers NUM_WORKERS] [--num_channels NUM_CHANNELS]
                [--max_epochs MAX_EPOCHS] [--random_mirror RANDOM_MIRROR]
                [--lr LR] [--batch_size BATCH_SIZE] [--optim {sgd,adam}]
                [--poly_exp POLY_EXP] [--cuda CUDA] [--gpus GPUS]
                [--resume RESUME] [--savedir SAVEDIR] [--logFile LOGFILE]

2. Run：

python train.py

Testing

1. Input arguments: (see full input arguments via python test.py --help):

usage: test.py [-h] [--model MODEL] [--dataRootDir DATAROOTDIR]
               [--dataset DATASET] [--num_workers NUM_WORKERS]
               [--batch_size BATCH_SIZE] [--checkpoint CHECKPOINT]
               [--cuda CUDA] [--gpus GPUS]

2. Run：

python test.py

Citation

If you use this code, please cite the following:

@ARTICLE{9583229,
  author={Wu, Zhipeng and Wang, Teng and Wang, Yingjie and Wang, Robert and Ge, Daqing},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Deep Learning for the Detection and Phase Unwrapping of Mining-Induced Deformation in Large-Scale Interferograms}, 
  year={2022},
  volume={60},
  number={},
  pages={1-18},
  doi={10.1109/TGRS.2021.3121907}}

Acknowledgement

Python, PyTorch, xiaoyufenfei

Statement

The code can only be used for personal academic research testing.