Hypernet

Introduction

This is a short demo of our hyperspectral image classification method based on convolutional neural networks. Hypernet was initially described in the ACM MM'15 paper titled Hyperspectral Image Classification with Convolutional Neural Networks.

The code demonstrates how to train the network for HSI classification on the Indian Pines dataset while using only 10% of the labeled samples. Alternatively, a pretrained network is provided for testing.

Citing Hypernet

If you find Hypernet useful in your research, please consider citing:

@inproceedings{Slavkovikj15Hypernet,
  author =       {Slavkovikj, Viktor and Verstockt, Steven and De Neve, Wesley
                  and Van Hoecke, Sofie and Van de Walle, Rik},
  title =        {{Hyperspectral Image Classification with Convolutional Neural
                  Networks}},
  booktitle =    {{Proceedings of the 23rd ACM International Conference on Multimedia}},
  year =         {2015},
  pages =        {1159--1162},
  numpages =     {4},
}

Prerequisites

• Theano
• Lasagne (tested version: '0.2.dev1')

Indian Pines dataset

To get the Indian Pines dataset run

./data/get_data.sh

Note: stats.py can be used to get more information about the number of samples used in the training and validation phase.

Train Hypernet

python hypernet.py net1

Test Hypernet

1. Download the pretrained network

./trained_models/get_net.sh

2. Test the network

python hypernet.py ./trained_models/net1.p.gz