# intelligent-matching-method-for-heterogeneous-remote-sensing-images-based-on-style-transfer

**Repository Path**: AZQZ/intelligent-matching

## Basic Information

- **Project Name**: intelligent-matching-method-for-heterogeneous-remote-sensing-images-based-on-style-transfer
- **Description**: Code and data
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-06-04
- **Last Updated**: 2022-07-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Intelligent Matching Method for Heterogeneous Remote Sensing Images Based on Style Transfer

#### Description
The code of D2-Net can be found at [https://github.com/mihaidusmanu/d2-net](http://github.com/mihaidusmanu/d2-net).
The code of LOFTR can be found at [https://zju3dv.github.io/loftr](http://zju3dv.github.io/loftr).
Style transfer image dataset, SA1, SA2, SA3 can be found at [https://pan.baidu.com/s/1KY_Z2iDDlhsewR9YUns1zQ](http://pan.baidu.com/s/1KY_Z2iDDlhsewR9YUns1zQ). 
Extraction code：pwl1. 


#### Prerequisites

torch>=0.4.1
torchvision>=0.2.1
dominate>=2.3.1
visdom>=0.1.8.3

For pip users, please type the command pip install -r requirements.txt


#### SCycleGAN train/test

- To view training results and loss plots, run `python -m visdom.server` and click the URL http://localhost:8097.
- Train a model:
```bash
#!./scripts/train_cyclegan.sh
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
```
To see more intermediate results, check out `./checkpoints/maps_cyclegan/web/index.html`.

- Test the model:
```bash
#!./scripts/test_cyclegan.sh
python test.py --dataroot ./datasets/maps/testA --name maps_cyclegan --model test --no_dropout
```
- The test results will be saved to a html file here: `./results/maps_cyclegan/latest_test/index.html`.

You may find useful information in [training/test tips](docs/tips.md).

#### Acknowledgments

Our code is inspired by [pytorch-CycleGAN]([https://github.com/junyanz/CycleGAN](http://github.com/junyanz/CycleGAN))