Codes for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”

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Deep Learning DSAMNet
Overview

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.


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.

    dataset

  • Comparisons to existing change detection datasets

    datasets


Experiments

Method: DSAMNet

model

Result

result


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}}
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Comments
  • The label of the dataset is not binary map.

    The label of the dataset is not binary map.

    Thank you for your generous sharing！

    I downloaded the CDD dataset mentioned in your paper, but found that the label of the CDD dataset was not binary map, as Issue in label dataset of 2018. Lebedev M A, Vizilter Y V, Vygolov O V, et al. Change detection in remote sensing images using conditional adversarial networks said.

    How did you deal with this situation ?

    thanks

    opened by yiyi-today 0
  • Shape error in IOU calculation

    Shape error in IOU calculation

    Hi there. In the evaluation step, I've got this error:

    	  File "train.py", line 119, in <module>
	    intr, unn = calMetric_iou(gt_value, result)
	  File "./DSAMNet/data_utils.py", line 17, in calMetric_iou
	    tp = np.sum(np.logical_and(predict == 1, label == 1))
	ValueError: operands could not be broadcast together with shapes (16,256,256) (16,64,256,256)

    BatchSize is 16 and I use default parameters. The output of the model (prob) is with the shape: torch.Size([16, 64, 256, 256]) and this is the cause of error.

    opened by farhadinima75 4
Owner
Mengxi Liu
Mengxi Liu
GitHub
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