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DSAMNet

The pytorch implementation for "A Deeply-supervised Attention Metric-based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection" on IEEE Transactions on Geoscience and Remote Sensing.

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Requirements

torch == 1.2.0 torchvision = 0.4.0

Dataset: SYSU-CD (download)

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.
Comparisons to existing change detection datasets

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Experiments

Method: DSAMNet

model

Result

result

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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}}