7. Conclusion

an important role in providing solution. GA belongs to a non-deterministic class of algorithms. The optimal solution we get from GA may vary each time we run our

Learning rule Back propagation + Levenberg-Marquardt

The number of layers 4

Input 17 Hidden1 25 Hidden2 25 Output 6 The initial weights and biases Random Activation functions Tangent sigmoid

Learning rate 0.01 Momentum constant 0.8 Mean-squared error 1e\_07

CNN requires a lot of training. Also, it requires a lot of data sets for training. A convolution is always a slower operation. Deeper the network, the longer is it's

The results of the entire work are verified with the help of the ground truth. Ground truth is the process done onsite, in which a "pixel" on a satellite image is compared to what is there in reality. It is done in order to verify the contents of the "pixel" on the image. For an image of 400 400 size, we have taken 500 points as ground truth data. By performing field visit, these data are collected. The outcome

A hypothetical case study is presented to show the application of land cover/land use mapping in real-life scenario. Assume the XXX company wants to plan their production center construction in Madurai city. For the production centre to be established, large area is needed and thus unoccupied areas in the city have to be

Defining the membership function remains a difficult task.

algorithm for the very same input data.

of each method is verified with those points.

6. A hypothetical case study

4.5.2 Convolutional neural network

MLP architecture and training parameters.

processing time.

Architecture

Training parameters

The number of neuron on the layers

Land Use Change and Sustainability

5. Field survey

68

4.5.3 ANFIS

Table 3.

The chapter focused on the methods used to obtain the perfect classification of the RS images. It discusses various methods used for feature extraction. Different feature extraction methods are discussed. After feature extraction, the number of obtained features is reduced using the feature subset selection methods. The best features are considered and the features which contribute less are neglected. The optimal features are thus taken into account for the classification process. The classification also discusses different techniques through which efficient results are obtained. The methods are implemented using LISS IV image of Madurai city. The classified outputs are shown wherever necessary, and accuracy assessments are also calculated. Thus, the chapter gives overall idea for handling RS image using optimal features.
