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Crop classification using supervised learning techniques
 

Crop classification using supervised learning techniques

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

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

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    Crop classification using supervised learning techniques Crop classification using supervised learning techniques Presentation Transcript

    • 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.
    • 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.
    • 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
    • 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.
    • 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 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%