MultiSpectral image has been classified using different kinds of neural networks and a complex valued neural network.

1. Crop-Type Classification using MLP, RBF
and CCELM
Objective
Multi Spectral Satellite Image Classification of 6 crop types using supervised
learning techniques ie; Multi Layer Perceptron Neural Networks
(MLPnn), Radial Basis Function Neural Networks (RBFnn) and Circular
Complex Extreme Learning Machine
Approach
The 4 bands of the hyper spectral data are used as the input to each of the 3
Neural Network Classifiers. Each of the implemented classifiers are trained by
Back-Propagation algorithm using the same ground truth. Each of the trained
classifiers are tested against the same dataset and the individual performance
are compared.

2. Crop-Type Classification using MLP,
RBF and CCELM
Multi spectral Data
• The multi spectral image comprises of
4 bands, namely, Red, Blue, Green
and infrared.
• High resolution, four band multi
spectral image of southern part of
India is used to derive the data
samples. It is of the dimension 1375 ×
5929 pixels and it covers an area of
2.748 × 7.973 km2. This image is first
divided into six distinct crop classes
namely, Sugarcane, Ragi, Paddy, Mulb
erry, Groundnut and Mango.

3. Crop-Type Classification using MLP, RBF
and CCELM
Multi-spectral data
The area selected for classification is the region surrounding Mysore district in
Karnataka, India. This region has the required crop coverage classes and it is also wide
spread and densely cultivated. It provides sufficient data samples to train the neural
classifiers for all the six classes. Therefore, it serves as suitable region for an
experimental study. Quick-Bird’s (operated by Digital Globe) multi-spectral (MSS)
image with the resolution of 2.4m has been used as inputs.
Class Class
no.
name
C1
C2
C3
C4
C5
C6
Sugarcane
Ragi
Paddy
Mulberry
Groundnut
Mango
Total
Number of
pixels for
training
100
100
100
100
100
100
600
Number of
pixels for
validation
400
400
400
400
400
400
2400
Parameter
No of crop types
Value
6
Samples for each
crop type (training)
100
Samples for each
crop type ( testing )
600
No of bands for
each sample
4

4. Crop-Type Classification using MLP, RBF
and CCELM
Methods:
1. Multilayer Perceptron Neural Network
(MLP-NN) divides the Input vector
space into different classes by means
of Hyper-planes, which is not an
efficient way of classification.
2.
Neural Network Structure Implemented
Radial Basis Function Neural Network
(RBF-NN) divides the input vector
space into multiple classes, using
hyper-spheres. This is a better and
efficient way of classification.

5. Crop-Type Classification using MLP, RBF
and CCELM
3. Circular Complex Extreme Learning Machine (CCELM): This uses complex
valued activation functions and complex valued weights. Hence for every
hidden neuron, there are 2 decision surfaces that are orthogonal to each
other. So, we have 4 decision boundaries and therefore, better classification.
Advantages of CCELM over MLP and RBF:
• MLP and RBF use Back Propagation algorithm for training, hence their
performance may be hindered from problem of local minima.
• To overcome this problem, we use Circular Complex Extreme Learning
Machine (CCELM)
• Extreme Learning Machine computes the required parameters by
formulating the problem of solving weights as problem of finding inverse
of given matrices. This greatly reduces the computational time.

6. Crop-Type Classification using MLP, RBF
and CCELM
Performance of MLPnn
C1
C2
C3
C4
C5
C6
Performance of RBFnn
C1 C2 C3 C4 C5 C6
395 0
0
0
0
5
0 400 0
0
0
0
0
0 400 0
0
0
0
0
0 397 0
3
0
0
0
0 400 0
169 7
0
0
0 224
Overall efficiency = 92.33%
C1
C2
C3
C4
C5
C6
C1
400
0
0
0
0
139
C2 C3
0
0
400
0
0
400
0
0
0
1
1
0
C4
0
0
0
399
0
0
C5 C6
0
5
0
0
0
0
0
1
399
0
0
260
Overall efficiency = 92.33%
Performance of CCELM
C1
C2
C3
C4
C5
C6
C1 C2 C3 C4 C5 C6
398 0
0
0
0
2
0 399 1
0
0
0
0
1 399 0
0
0
0
0
0 398 0
2
0
0
0
0 400 0
18
0
2
0
0 380
Overall efficiency = 98.9%