/SAE-3DDRN

This is an implementation of the SAE-3DDRN network presented in "A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification"

Primary LanguagePython

SAE-3DDRN

This is an implementation of the SAE-3DDRN network presented in "A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification". This implementation is purely based on the paper without any access to the originally implemented network.

Tested with

Python 3.8 and 3.9

Pytorch 1.9.0

CPU and GPU

Run the SAE-3DDRN

Please set your parameters in train.py or test.py before running them.

To train, run:

# Trains network multiple times (see parameters in file)
python train.py

To test, run:

# Tests all runs saved in a given directory
python test.py

About datasets

The datasets are available here. The used datasets for this implementation are: PaviaU, Indian Pines and Salinas.

Config file

Please refer to the config file config.yaml for details about the possible configurations of the network/training/testing.