This repository provides three Jupyter notebooks streamlining the application of deep learning algorithms for hyperspectral image classification. The three classification methods are based on Multi-Layer Perceptron (MLP), 2-Dimensional Convolutional Neural Network (2-D CNN), and 3-D Convolutional Neural Network models (3-D CNN). The different stages of the analysis including pre-processing of raw data, sampling of train and test datasets, dimensionality reduction, data rescaling, model training and prediction, and a set of result visualization tools are made possible through the 'img_util' module.
The notebooks were tested in an environment using python 3.6.6, numpy 1.15.2, keras 2.2.2, scipy 1.1.0, matplotlib 2.2.3.
The Indian Pine hyperspectral dataset and its corresponding ground truth data can be downloaded from the following website: http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
This project is licensed under the MIT License - see the LICENSE.md file for details.