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https://github.com/wgcban/SemiCD
Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
https://github.com/wgcban/SemiCD
change-detection consistency levir-cd remote-sensing segmentation semi-supervised supervised-learning whu-cd
Last synced: 3 months ago
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Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
- Host: GitHub
- URL: https://github.com/wgcban/SemiCD
- Owner: wgcban
- Created: 2022-02-03T15:15:06.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-09T13:49:39.000Z (10 months ago)
- Last Synced: 2024-07-23T03:39:19.401Z (4 months ago)
- Topics: change-detection, consistency, levir-cd, remote-sensing, segmentation, semi-supervised, supervised-learning, whu-cd
- Language: Python
- Homepage: https://www.wgcban.com/research#h.ar24vwqlm021
- Size: 2.3 MB
- Stars: 117
- Watchers: 1
- Forks: 19
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-remote-sensing-change-detection - Bandara W G C, Patel V M. Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
README
## Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
[`Wele Gedara Chaminda Bandara`](https://www.wgcban.com/), and [`Vishal M. Patel`](https://engineering.jhu.edu/vpatel36/sciencex_teams/vishalpatel/)
[](https://paperswithcode.com/sota/semi-supervised-change-detection-on-levir-cd?p=revisiting-consistency-regularization-for-1)
[](https://paperswithcode.com/sota/semi-supervised-change-detection-on-levir-cd-1?p=revisiting-consistency-regularization-for-1) [](https://paperswithcode.com/sota/semi-supervised-change-detection-on-levir-cd-2?p=revisiting-consistency-regularization-for-1) [](https://paperswithcode.com/sota/semi-supervised-change-detection-on-levir-cd-3?p=revisiting-consistency-regularization-for-1)
[](https://paperswithcode.com/sota/semi-supervised-change-detection-on-whu-5?p=revisiting-consistency-regularization-for-1)
[](https://paperswithcode.com/sota/semi-supervised-change-detection-on-whu-10?p=revisiting-consistency-regularization-for-1)
[](https://paperswithcode.com/sota/semi-supervised-change-detection-on-whu-20?p=revisiting-consistency-regularization-for-1)
[](https://paperswithcode.com/sota/semi-supervised-change-detection-on-whu-40?p=revisiting-consistency-regularization-for-1)
:open_book: :open_book: :open_book: :open_book: View paper [`here`](https://arxiv.org/abs/2204.08454).
:bookmark: :bookmark: :bookmark: View project page [`here`](https://www.wgcban.com/research#h.ar24vwqlm021).
This repocitory contains the official implementation of our paper: **Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images**.
## :speech_balloon: Requirements
This repo was tested with `Ubuntu 18.04.3 LTS`, `Python 3.8`, `PyTorch 1.1.0`, and `CUDA 10.0`. But it should be runnable with recent PyTorch versions >=1.1.0.
The required packages are `pytorch` and `torchvision`, together with `PIL` and `opencv` for data-preprocessing and `tqdm` for showing the training progress.
```bash
conda create -n SemiCD python=3.8
conda activate SemiCD
pip3 install -r requirements.txt
```
## :speech_balloon: Datasets
We use two publicly available, widely-used CD datasets for our experiments, namely [`LEVIR-CD`](https://justchenhao.github.io/LEVIR/) and [`WHU-CD`](http://gpcv.whu.edu.cn/data/building_dataset.html). Note that LEVIR-CD and WHU-CD are building CD datasets.
As we described in the paper, following previous works [`ChangeFormer`](https://github.com/wgcban/ChangeFormer) and [`BIT-CD`](https://github.com/justchenhao/BIT_CD) on supervised CD, we create non-overlapping patches of size 256x256 for the training. The dataset preparation codes are written in MATLAB and can be found in ``dataset_preperation`` folder. These scripts will also generate the supervised and unsupervised training scripts that we used to train the model under diffrent percentage of labeled data.
**Instead, you can directely download the processed LEVIR-CD and WHU-CD through the following links. Save these datasets anywhere you want and change the ``data_dir`` to each dataset in the corresponding ``config`` file.**
The processed LEVIR-CD dataset, and supervised-unsupervised splits can be downloaded [`here`](https://www.dropbox.com/s/18fb5jo0npu5evm/LEVIR-CD256.zip?dl=0).
The processed WHU-CD dataset, and supervised-unsupervised splits can be downloaded [`here`](https://www.dropbox.com/s/r76a00jcxp5d3hl/WHU-CD-256.zip?dl=0).
## :speech_balloon: Training
To train a model, first download processed dataset above and save them in any directory you want, then set `data_dir` to the dataset path in the config file in ``configs/config_LEVIR.json``/``configs/config_WHU.json`` and set the rest of the parameters, like ``experim_name``, ``sup_percent``, ``unsup_percent``, ``supervised``, ``semi``, ``save_dir``, ``log_dir`` ... etc., more details below.
### :point_right: Training on LEVIR-CD dataset
Then simply run:
```bash
python train.py --config configs/config_LEVIR.json
```
The following table summarizes the **required changes** in ``config`` file to train a model ``supervised`` or ``unsupervised`` with different percentage of labeled data.
| Setting | Required changes in `config_LEVIR.json` file |
| --- | --- |
| Supervised - 5% labeled data | Experiment name: `SemiCD_(sup)_5`, sup_percent= `5`, model.supervised=`True`, model.semi=`False` |
| Supervised - 10% labeled data | Experiment name: `SemiCD_(sup)_10`, sup_percent= `10`, model.supervised=`True`, model.semi=`False` |
| Supervised - 20% labeled data | Experiment name: `SemiCD_(sup)_20`, sup_percent= `20`, model.supervised=`True`, model.semi=`False` |
| Supervised - 40% labeled data | Experiment name: `SemiCD_(sup)_40`, sup_percent= `40`, model.supervised=`True`, model.semi=`False` |
| Supervised - 100% labeled data (**Oracle**) | Experiment name: `SemiCD_(sup)_100`, sup_percent= `100`, model.supervised=`True`, model.semi=`False` |
| | |
| Semi-upervised - 5% labeled data | Experiment name: `SemiCD_(semi)_5`, sup_percent= `5`, model.supervised=`Flase`, model.semi=`True` |
| Semi-upervised - 10% labeled data | Experiment name: `SemiCD_(semi)_10`, sup_percent= `10`, model.supervised=`Flase`, model.semi=`True` |
| Semi-upervised - 20% labeled data | Experiment name: `SemiCD_(semi)_20`, sup_percent= `20`, model.supervised=`Flase`, model.semi=`True` |
| Semi-upervised - 40% labeled data | Experiment name: `SemiCD_(semi)_40`, sup_percent= `40`, model.supervised=`Flase`, model.semi=`True` |
### :point_right: Training on WHU-CD dataset
Please follow the same changes that we outlined above to WHU-CD dataset as well.
Then simply run:
```bash
python train.py --config configs/config_WHU.json
```
### :point_right: Training with cross-domain data (i.e., LEVIR as supervised and WHU as unsupervised datasets)
In this case we use LEVIR-CD as the supervised dataset and WHU-CD as the unsupervised dataset. Therefore, you need to update the ``train_supervised`` ``data_dir`` as the path to LEVIR-CD dataset, and ``train_unsupervised`` ``data_dir`` as the path to WHU-CD dataset in ``config_LEVIR-sup_WHU-unsup.json``. Then change the ``sup_percent`` in the config file as you want and then simply run:
```bash
python train.py --config configs/config_LEVIR-sup_WHU-unsup.json
```
### :point_right: Monitoring the training log via TensorBoard
The log files and the `.pth` checkpoints will be saved in `saved\EXP_NAME`, to monitor the training using tensorboard, please run:
```bash
tensorboard --logdir saved
```
To resume training using a saved `.pth` model:
```bash
python train.py --config configs/config_LEVIR.json --resume saved/SemiCD/checkpoint.pth
```
**Results**: The results will be saved in `saved` as an html file, containing the validation results,
and the name it will take is `experim_name` specified in `configs/config_LEVIR.json`.
## :speech_balloon: Inference
For inference, we need a pretrained model, the pre-chage and pos-change imags that we wouldlike to dtet changes and the config used in training (to load the correct model and other parameters),
```bash
python inference.py --config config_LEVIR.json --model best_model.pth --images images_folder
```
Here are the flags available for inference:
```
--images Folder containing the jpg images to segment.
--model Path to the trained pth model.
--config The config file used for training the model.
```
## :speech_balloon: Pre-trained models
Pre-trained models can be downloaded from the following links.
Pre-trained models on LEVIR-CD can be downloaded from [`here`](https://www.dropbox.com/sh/0m8t6dq37f11ukx/AAAgTgIxr_eyJJeHWqZ_SRVYa?dl=0).
Pre-trained models on WHU-CD can be downloaded from [`here`](https://www.dropbox.com/sh/oyn3d8hyz6qnzm5/AAAct3ueZ39xYINQbbO0oSJ_a?dl=0).
Pre-trained models for cross-dataset experiments can be downloaded from [`here`](https://www.dropbox.com/sh/mvszluw944jvhc3/AAB-eR-stgVsjmNSvzZ5Hlqqa?dl=0).
## :speech_balloon: Citation
If you find this repo useful for your research, please consider citing the paper as follows:
```
@misc{bandara2022revisiting,
title={Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images},
author={Wele Gedara Chaminda Bandara and Vishal M. Patel},
year={2022},
eprint={2204.08454},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
#### Acknowledgements
- This code is based on [CCT](https://github.com/yassouali/CCT).
- Code structure was based on [Pytorch-Template](https://github.com/victoresque/pytorch-template/blob/master/README.m)
- ResNet backbone was downloaded from [torchcv](https://github.com/donnyyou/torchcv)