S3ANet: Spatial-Scattering Separated Attention Network for Polarimetric SAR Image Classification

Environment

Pytorch 0.4.1; Python 3.6.3

Requirements:

Pytorch
Torchvision
Numpy
PIL
matplotlib
pandas
sklearn
tqdm
timeit

Datasets

Datatsets: The datasets Oberpfaffenhofen and Flevoland adopted in our paper "S3ANet: Spatial-Scattering Separated Attention Network for Polarimetric SAR Image Classification" can be downloaded by the website "https://earth.esa.int/web/polsarpro/data-sources/sample-datasets".
Meanwhile, the corresponding ground truth can be downloaded by the website "https://github.com/fudanxu/CV-CNN".
The datasets Sanfranciso and corresponding groundtruth can be found in "https://github.com/liuxuvip/Polarimetric-Scattering-Coding".
Updata: The San_padding36.npy has been upload in the following Baiduyun link.
https://pan.baidu.com/s/1AWK_9JfOwHK74e3faUEIxA password：qujc

Training

run main.py

Testing

run testReports.py

SSSNet

Classification results

Oberpfaffenhofen, E-SAR, L-Band