TSMF-Net

Language: English, 简体中文

Cite

@article{liao2022tsmf,
  title={A Two-Stage Mutual Fusion Network for Multispectral and Panchromatic Image Classification},
  author={Liao, Yinuo and Zhu, Hao and Jiao, Licheng and Li, Xiaotong and Li, Na and Sun, Kenan and Tang, Xu and Hou, Biao},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={60},
  pages={1--18},
  year={2022},
  publisher={IEEE}
}

Environment

Env/Package	Version	Env/Package	Version
python	3.6.10	libtiff	0.4.2
cuda	10.1	numpy	1.19.2
torch	1.3.1	pillow	8.0.1
torchvision	0.4.2	scipy	1.5.4
opencv	4.4.0.46	hdf5storage	0.1.18
gdal	3.0.2	h5py	3.1.0

Set up the environment by requirements.txt or jianchao.yaml, which are both in extra folder.

Preprocess

1) get_vec.py

Input: msf.tif and pan.tif

Detail: get_vec.py does 2x upsampling on msf, reshape $(H,W,4)\to(2H,2W,4)$; And it does 2-split operation on pan, reshape $(4H,4W,1)\to(2H,2W,4)$, both have the same shape at time

Then call to_tensor() function to normalize both of them, making data type float32 and data range [0,1]

Finally they will be flattened, reshape $(2H,2W,4)\to(2H\times2W,4)$

Output: msf.mat and pan.mat

2) get_para.m

Input: msf.mat and pan.mat

Detail: Let the weight parameter of msf be $\alpha_i\quad(i=1,2,3,4)$ and the weight parameter of pan be $\beta_i\quad(i=1,2,3,4)$. Solve the following convex optimization problem:

$$ \mathop{\min}\limits_{\alpha_i,\beta_i} \Vert \sum_{i=1}^4 \alpha_i M_i - \sum_{i=1}^4 \beta_i P_i \Vert_2^2 $$

$$ s.t. \alpha_i,\beta_i>0, \sum_{i=1}^4 \beta_i=1 $$

Output: Run time sj, weight parameters para (i.e. $\alpha_i$ and $\beta_i$) and minimum value val

Caution: This MATLAB script depends on icanfast.m, please be careful not to delete or move it

3) img_fusion.py

Input: msf.tif, pan.tif , $\alpha_i$ and $\beta_i$

Detail: Refer the paper for details

Output: msf_f.npy and pan_f.npy

Caution: $\alpha_i$ and $\beta_i$ need to be modified manually in line 111,112

Train&Test

train.py

Input: msf_f.npy, pan_f.npy and label.mat

Detail: Train&Test in one

Output: .pkl model named after AA

Visualize

draw.py

Input: msf_f.npy, pan_f.npy and label.mat

Detail: Enter 0 for half and 1 for full

Output: xx_half.png and xx_full.png