Crop-Type Classification using MLP, RBF and CCELM

ObjectiveMulti 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

ApproachThe 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.

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, Mulberry, Groundnut and Mango.

Crop-Type Classification using MLP, RBF and CCELM

Multi-spectral dataThe 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.

Parameter Value

No of crop types 6

Samples for each crop type (training)

100

Samples for each crop type ( testing )

600

No of bands for each sample

4

Class no.

Classname

Number of pixels for training

Number of pixels forvalidation

C1 Sugarcane 100 400C2 Ragi 100 400C3 Paddy 100 400C4 Mulberry 100 400C5 Groundnut 100 400C6 Mango 100 400 Total 600 2400

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. 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.

Neural Network Structure Implemented

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.

Crop-Type Classification using MLP, RBF and CCELM

Performance of MLPnn Performance of RBFnn

Overall efficiency = 92.33% Overall efficiency = 92.33%

Performance of CCELM

Overall efficiency = 98.9% C1 C2 C3 C4 C5 C6

C1 398 0 0 0 0 2C2 0 399 1 0 0 0C3 0 1 399 0 0 0C4 0 0 0 398 0 2C5 0 0 0 0 400 0C6 18 0 2 0 0 380

C1 C2 C3 C4 C5 C6C1 395 0 0 0 0 5C2 0 400 0 0 0 0C3 0 0 400 0 0 0C4 0 0 0 397 0 3C5 0 0 0 0 400 0C6 169 7 0 0 0 224

C1 C2 C3 C4 C5 C6C1 400 0 0 0 0 5C2 0 400 0 0 0 0C3 0 0 400 0 0 0C4 0 0 0 399 0 1C5 0 0 1 0 399 0C6 139 1 0 0 0 260