Super-resolution domain adaptation networks for semantic segmentation via pixel and output level aligning

Pytorch implementation of our method for adapting semantic segmentation from the low-resolution remote sensing dataset (source domain) to the high-resolution remote sensing dataset.

Contact: Zhenjie Tang (tangzhenjie.hebut@gmail.com)

Paper

Wu J, Tang Z, Xu C, Liu E, Gao L and Yan W (2022), Super-resolution domain adaptation networks for semantic segmentation via pixel and output level aligning. Front. Earth Sci. 10:974325. doi: 10.3389/feart.2022.974325

Please cite our paper if you find it useful for your research.

Example Results

Quantitative Reuslts

Installation

• Install Pytorch 1.3.0 from http://pytorch.org with python 3.6 and CUDA 10.1

• Clone this repo

git clone https://github.com/tangzhenjie/SRDA-Net
cd SRDA-Net

Dataset

• Download the Massachusetts Buildings Dataset Training Set as the source domain, and put it ./datasets folder

• Download the Inria Aerial Image Labeling Dataset as the target domain, and put it to ./datasets folder.

• Create the Mass-Inria dataset

cd datasets
python create_train_oneclass.py
python create_val_oneclass.py

Testing

• Download the checkpoint 提取码：dw0p to ./checkpoints/mass_inria/

• run

cd datasets
python val.py --name mass_inria --dataroot ./datasets/mass-inria  --model srdanet_step2 --num_classes 2 --dataset_mode srdanetval --resize_size 188

Training Examples

• pre-training the SRDA-Net

cd datasets
python train.py --name mass_inria_step1 --dataroot ./datasets/mass-inria  --model srdanet_step1 --num_classes 2 --dataset_mode srdanet --A_crop_size 114 --B_crop_size 380

• copy the weight of re-training srdanet to the ./checkpoints/mass_inria_step2/ then, run (num is the epoch num of pre-training)

cd datasets
python train.py --name mass_inria_step2 --dataroot ./datasets/mass-inria  --model srdanet_step2 --num_classes 2 --dataset_mode srdanet --A_crop_size 114 --B_crop_size 380 --epoch num

Acknowledgment

This code is heavily borrowed from CycleGAN