custom-computing-ic/SpecPatConv3D-Network

An implementation of the neural network described in "Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification"

SpecPatConv3D Network

📌Description

This is an implementation code for Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification published at International Geoscience and Remote Sensing symposium 2019.

If you have any question related to the paper or the code, feel free to contact Ringo Chu (ringo.chu.16@ucl.ac.uk)

This is a neural network architecture using 3D Convolutional neural network with partitioning on \lamba dimension to classify unlabelled pixel on Hyperspectral images, and achieving the state-of-art performance on classification with labelled dataset on Indian Pines, Salinas scenes.

🗂Prerequisites

• Python3.7
• TensorFlow 1.10
• numpy
• scipy
• argparse
• tqdm
• (Optional) CUDA v9.0 and cuDNN v7.0
• (Optional) TensorFlow GPU

We recommend you to create a Python Virtual environment, issue the command pip install -r requirement.txt in your command prompt/terminal to install all dependencies required.

If you use Anaconda, you could issue this command to create a virtual environment: conda env create --file SpecPatConv3D.yml

The code will be soon updated to TensorFlow 2.0 and will also be providing a PyTorch implementation, hold on tight and get updated!

All programs are texted under Ubuntu, MacOS, Windows10. For windows user, you'll need to download dataset manually with instructions below.

🛠General guidance using this repository

• Acquire the Dataset (Do this Step if you're using Windows)

  • Hyperspectral Datasets used in this research work Several hyperspectral datasets are available on the UPV/EHU wiki. Download IndianPines_corrected.mat or Salinas_corrected.mat or KSC.mat or Botswana.mat and unzip in the folder data

• Preprocess and prepare dataset

  • Run the following command: python preprocess.py --data Indian_pines --train_ratio 0.15 --validation_ratio 0.05
  • --data : Choose from Indian Pines, Salinas, KSC or Botswana

• Train the model

  • Run the following command: python train.py --data Indian_pines --epoch 650
  • --data : Choose from Indian Pines, Salinas, KSC or Botswana

• Evaluate the model

  • Run this command: python evaluate.py --data Indian_pines

📝Citation

If you find paper helpful, please consider citing us.❤

@inproceedings{igarss19chu,
  author    = {Ringo S.W. Chu and Ho-Chung Ng and Xiwen Wang and Wayne Luk},
  title     = {Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification},
  booktitle = {{IEEE Geoscience and Remote Sensing symposium}},
  year      = {2019}
}