CACRWNet

Context-aware and Channel re-weighting network with Noise suppression for Remote sensing scene classification Links to papers

Instruction

• split.py

Once you've obtained the necessary dataset, use split.py to proportionally partition the dataset into training and test sets. The code simply needs to change the source and new file paths and divide the ratio.

• reshape.py

Its role is to crop the image size to 224×224, all you have to do is alter the source file directory and the new file location, and you can also change the size of the crop image.

• train.py

Change the path of the training and test sets first, then the number of experiments.

num_epochs = config.NUM_EPOCHS

num = 1

The optimizer can also be customized to meet your needs.

• config.py

You can change the data set category, training batch size, number of training rounds, and path to store the model from config.py.

Datasets

• UC Merced Land Use Dataset:

http://weegee.vision.ucmerced.edu/datasets/landuse.html

• AID Dataset:

https://captain-whu.github.io/AID/

• NWPU RESISC45:

http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html

Enviroment

python = 3.8.10

torch = 1.10.0

cuda = 11.3

If you want to use this paper, please include a citation