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Classification of Hyperspectral Satellite Image Using Deep Convolutional Neural Networks

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# 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  	|

![alt text](https://raw.githubusercontent.com/subhajitchaudhury/deephypercnn/master/Assets/Image_results.png "Classification results on both dataset. Only 16 non-zero labels are tested")

# 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

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