https://github.com/liumency/DSAMNet

Codes for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”
https://github.com/liumency/DSAMNet

Last synced: about 2 months ago
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Codes for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”

• Host: GitHub
• URL: https://github.com/liumency/DSAMNet
• Owner: liumency
• Created: 2021-09-13T06:02:56.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-09-17T15:08:20.000Z (about 1 year ago)
• Last Synced: 2024-07-23T03:39:47.347Z (about 2 months ago)
• Language: Python
• Size: 405 KB
• Stars: 56
• Watchers: 1
• Forks: 13
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-remote-sensing-change-detection - Shi Q, Liu M, Li S, et al. A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection

README

        
# DSAMNet

The pytorch implementation for "[A Deeply-supervised Attention Metric-based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection](https://ieeexplore.ieee.org/document/9467555)" on [IEEE Transactions on Geoscience and Remote Sensing](https://ieeexplore.ieee.org/xpl/aboutJournal.jsp?punumber=36).




## Requirements

torch == 1.2.0

torchvision = 0.4.0

## Dataset: SYSU-CD ([download](https://github.com/liumency/SYSU-CD))

- The dataset contains 20000 pairs of 0.5-m aerial images of size 256×256 taken between the years 2007 and 2014 in Hong Kong. 

- The main types of changes in the dataset include: (a) newly built urban buildings; (b) suburban dilation; (c) groundwork before construction; (d) change of vegetation; (e) road expansion; (f) sea construction.

   ![dataset](images/dataset.jpg)

- Comparisons to existing change detection datasets 

   

   ![datasets](images/datasets.jpg)




## Experiments

### Method: DSAMNet

   ![model](images/model.jpg)

### Result

   ![result](images/result.jpg)




## Citation

If you find our work useful for your research, please consider citing our paper:

```

@ARTICLE{shi21deeply,

  author={Shi, Qian and Liu, Mengxi and Li, Shengchen and Liu, Xiaoping and Wang, Fei and Zhang, Liangpei},

  journal={IEEE Transactions on Geoscience and Remote Sensing}, 

  title={A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection}, 

  year={2021},

  volume={},

  number={},

  pages={1-16},

  doi={10.1109/TGRS.2021.3085870}}

```