cucrobert / deephypercnn

Classification of Hyperspectral Satellite Image Using Deep Convolutional Neural Networks

deephypercnn

Classification of Hyperspectral Satellite Image Using Deep Convolutional Neural Networks. This is re-implementation of the paper

[1] K. Makantasis, K. Karantzalos, A. Doulamis and N. Doulamis, "Deep supervised learning for hyperspectral data classification through convolutional neural networks," 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, 2015, pp. 4959-4962.

Method details

1. For each non-zero labelled pixel, we extract 5 x 5 x c neighbourhood and corresponding label.

2. Dimensionality reduction using PCA is performed. Final dimension is 5 x 5 x cr.

3. Training using CNN is performed with the following architecture: conv1-conv2-conv3-conv4-hidden1-hidden2-16way-softmax

4. Training : testing split ratio is maintained at 0.8 : 0.2

Results

Table 1 : Comparison of accuracy for various classification methods

Dataset	No. of Components	RBF-SVM	CNN [1]	Our CNN
Indian Pines	30	82.79	98.88	98.94
Pavia University	10	93.94	99.62	99.66

Implementation

1. Data preparation : Matlab (Mat file)

-Download publicly available data mat files from following link and place them in /Matlab-Sat-Data/data/

http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes

-Then run /Matlab-Sat-Data/script_prep_data.m

2. CNN classification : Theano + Lasagne+ Nolearn

-Run train.py for training and testing accuracy

For PCA, this matlab file exchange implementation was used: https://jp.mathworks.com/matlabcentral/fileexchange/38300-pca-and-ica-package/content/pca_ica/myPCA.m

MIT License Copyright (c) 2016 Subhajit Chaudhury