4.4 Classification using multilayer perceptron layer

The multilayer perceptron (MLP) layer realizes intelligent classification using features from the wavelet layer. The training parameters of the MLP are shown in Table 3. These parameters were selected to give best performance, after several experiments, such as the number of hidden layers, size of the hidden layers, value of the moment constant and learning rate, and type of the activation functions.

## 4.5 Limitations of different methodologies

#### 4.5.1 Genetic algorithm

In GA, the selection of wrong fitness value may affect the solution of the problem. Other parameters like population size, mutation and crossover also plays


investigated. Also, it sends out waste material that is toxic and should not be present in the urban areas. The products, which the company produces, are sent to other parts of the country and some are exported. So, road routes also have to be checked. So, initially a place has to be selected and a plan to be made accordingly. In order to plan the construction, it acquires the satellite image of Madurai city. Then the features are obtained using wavelet feature extraction method, and the classified output is obtained using adaptive neuro-fuzzy inference system classification. The classified image can be clearly understood and can be given to the construction planning team for their further processing. Here, also in addition if the PAN image

Land Cover/Land Use Mapping Using Soft Computing Techniques with Optimized Features

DOI: http://dx.doi.org/10.5772/intechopen.86218

of the Madurai city and MS image of the Madurai city are fused and then if classification is performed, it would yield still better classification results.

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

7. Conclusion

optimal features.

Author details

Selvaraj Rajesh<sup>1</sup>

Sivakasi, India

69

and Technology, Sivakasi, India

provided the original work is properly cited.

\* and Gladima Nisia T.2

\*Address all correspondence to: srajesh@mepcoeng.ac.in

1 Department of Information Technology, Mepco Schlenk Engineering,

2 Department of Computer Science Engineering, AAA College of Engineering

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Table 3.

MLP architecture and training parameters.

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 algorithm for the very same input data.
