MinZHANG-WHU/DASNet

DASNet: Dual attentive fully convolutional siamese networks for change detection of high-resolution satellite images

The architecture:

Requirements

• Python3.6
• Pytorch.03 (see: pytorch installation instuctions)
• torchvision

Datasets

This repo is built for remote sensing change detection. We report the performance on two datasets.

• CDD

• paper: Change detection in remote sensing images using conditional adversarial networks

• BCDD

• paper: Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

Directory Structure

File Structure is as follows:

$T0_image_path/*.jpg
$T1_image_path/*.jpg
$ground_truth_path/*.jpg

Pretrained Model

The backbone model and pretrained models for CDD and BCDD can be download from [googledriver] [baidudisk] password:86of

Training

cd $CD_ROOT
python train.py

Citation

Bibtex
@article{chen2020dasnet,
    title={DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images},
    author={Chen, Jie and Yuan, Ziyang and Peng, Jian and Chen, Li and Huang, Haozhe and Zhu, Jiawei and Lin, Tao and Li, Haifeng},
    journal={arXiv:2003.03608},
    DOI = {arXiv:2003.03608},
    year={2020},
    type = {Journal Article}
}

Endnote
%0 Journal Article
%A Chen, Jie
%A Yuan, Ziyang
%A Peng, Jian
%A Chen, Li
%A Huang, Haozhe
%A Zhu, Jiawei
%A Lin, Tao
%A Li, Haifeng
%D 2020
%T DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
%B arXiv:2003.03608
%R arXiv:2003.03608
%! DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images