RegDA

KO | EN

gitlite — search

#hacktoberfest #react #ai #typescript #android #rust #linux #macos #java #awesome #pytorch #mcp #ios #open-source

(★ 99)

The official implementation for "Learn from Segment Anything Model: Local Region Homogenizing for Cross-domain Remote Sensing Image Segmentation".

RegDA Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<h2 align="center">Learn from Segment Anything Model: Local Region Homogenizing for Cross-domain 
Remote Sensing Image Segmentation</h2>

<h5 align="center">
  by <a href="https://scholar.google.com/citations?user=LXlWdyQAAAAJ&hl=zh-CN">Wang Liu</a>,
  <a href="https://scholar.google.com/citations?hl=zh-CN&user=IYUlx_8AAAAJ">Puhong Duan</a>, 
  <a href="https://www.researchgate.net/profile/Zhuojun-Xie-6">Zhuojun Xie</a>, 
  <a href="https://scholar.google.com/citations?hl=zh-CN&user=5XOeLZYAAAAJ">Xudong Kang</a>, 
  <a href="https://scholar.google.com/citations?user=PlBq8n8AAAAJ&hl=zh-CN">Shutao Li</a>
</h5>

<h5 align="left">This is the official implementation for 
<a href="https://ieeexplore.ieee.org/abstract/document/10642007">RegDA</a>.
  
---------------------

<div align=center><img src="asserts/overview-regda.png"></div>
<p align="center">Fig. 1 An overview of the proposed RegDA.</p>

## Getting Started

### Environment:
- conda create -n regda python=3.8
- source activate regda
- pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
- conda install pytorch-scatter -c pyg
- pip install -r requirement.txt
- pip install -e .

### Prepare datasets

#### 1. Generate from raw:

- Download the raw datasets <a href="https://www.isprs.org/education/benchmarks/UrbanSemLab/2d-sem-label-potsdam.aspx">here</a>.
- Run the preprocess script in ./convert_datasets/ to crop train, val, test sets:\
`python convert_datasets/convert_potsdam.py`\
`python convert_datasets/convert_vaihingen.py`
- Generate local regions by running \
`python tools/seg_everything.py`
#### 2. Alternatively, you can also download the processed data
- Download the processed datasets <a href="https://pan.baidu.com/s/1rWHSgRpSVPlLt5_bykHCOg?pwd=6th5">here</a>.
- reorganize the directory tree.
#### 3. The prepared data is formatted as follows:
"\
-&nbsp;data\
&nbsp;&nbsp;&nbsp;|---&nbsp;IsprsDA\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---Potsdam\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---&nbsp;ann_dir\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---&nbsp;img_dir\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---&nbsp;reg_dir\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---Vaihingen\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---&nbsp;ann_dir\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---&nbsp;img_dir\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|---&nbsp;reg_dir\
"

### Evaluate the trained RegDA models.\
Download the pre-trained [<b>weights</b>](https://pan.baidu.com/s/1rWHSgRpSVPlLt5_bykHCOg?pwd=6th5) and logs.
#### 1. on Vaihingen (IRRG) -> Potsdam (IRRG) task
Run evaluating: 
```python
python tools/eval.py --config-path st.regda.2potsdam --ckpt-path log/regda/2potsdam/ssl/Potsdam_best.pth --test 1
```
#### 2. on Potsdam (IRRG) -> Vaihingen (IRRG) task
Run evaluating: 
```python
python tools/eval.py --config-path st.regda.2vaihingen --ckpt-path log/regda/2vaihingen/ssl/Vaihingen_best.pth --test 1
```

### Train the RegDA
```bash 
bash runs/regda/run_2potsdam.sh
```
```bash 
bash runs/regda/run_2vaihingen.sh
```

### Inference single file
```bash 
python tools/infer_single.py st.regda.2potsdam log/regda/ssl/Potsdam_best.pth [image-path] --save-dir [save-dir-path]
```

### Cite
If you are interested in this work, please cite it as follows:
```bibtxt
@INPROCEEDINGS{10642007,
  author={Liu, Wang and Duan, Puhong and Xie, Zhuojun and Kang, Xudong and Li, Shutao},
  booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, 
  title={Learn From Segment Anything Model: Local Region Homogenizing for Cross-Domain Remote Sensing Image Segmentation}, 
  year={2024},
  pages={8351-8354},
  doi={10.1109/IGARSS53475.2024.10642007}
}
```

---------------------

There are some differences compared with the published paper.</h5>

- The denoising approach in this repository is re-weighting, while in the paper is voting;
- A class-frequency threshold is utilized to guide the pseudo-label homogenizing. 
For each local region, if the class with a larger frequency than this threshold, 
it will be employed for homogenizing;
- The LRH is also utilized in the aligning stage.

RegDA

RegDA Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

RegDA

RegDA Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README