ZhengJianwei2 / Oct-MCNN-HS

Oct-MCNN-HS

Papers

• MCNN-CP: Hyperspectral Image Classification Using Mixed Convolutions and Covariance Pooling (TGARS 2021) paper and source_code
• Oct-MCNN-HS: 3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification With Limited Samples (Submitted)

1. Environment setup

This code has been tested on on a personal laptop with Intel i7-9750H 2.6-GHz processor, 32-GB RAM, and an NVIDIA GTX1650 graphic card, Python 3.6, tensorflow_gpu-1.14.0, Keras-2.2.4, CUDA 10.0, cuDNN 7.6. Please install related libraries before running this code:

pip install -r requirements.txt

2. Download the datesets:

• IP: Indian Pines corrected and Indian Pines gt
• UH: University of Houston 2018
• UP: University of Pavia corrected and University of Pavia gt
• SA: Salinas Scene corrected and Salinas Scene gt

and put them into data directory.

3. Download the models (loading models):

• models code:

and put them into models directory.

4. Download the pretrained_models (loading model parameters):

• IP: Indian Pines code:
• UH: University of Houston code:
• UP: University of Pavia code:
• SA: Salinas Scene code:

and put them into pretrained_models directory.

5. Test

python validate.py                
--dataset IP                       # dataset_name
--model Oct-MCNN-HS                # model_name
--ratio 0.99                       # test_ratio

The testing result will be saved in the classification_report.txt.

6. Cite