4.1 Classification based on Mahalanobis distance

In this method, the decomposition for test texture image is done using DWPD. In the same manner, another set of features are obtained and compared with the obtained feature values. The class of textures is represented as C, the mean signature of class C is represented with mc, and the Mahalanobis distance is given by

$$D^2(\mathbf{x}\_i, \mathbf{C}) = (\mathbf{x}\_i - m\_c) \sum (\mathbf{x}\_i - m\_c) \tag{13}$$

If the distance D(i) is smallest, then the test texture image is classified as ith texture [15, 21, 47]. Features are obtained using many wavelet filters, and it is followed using classification process [14, 28, 44]. The overall, user, producer, and kappa accuracy indices obtained for the different wavelet filters presented in Table 2 show that the DB2 wavelet filter gives superior results than the other wavelet filters. Thus, the DB2 wavelet filter will be more useful for land cover/land use mapping.

### 4.2 Classification based on adaptive neuro-fuzzy inference system (ANFIS)

The adaptive network-based fuzzy inference system (ANFIS) is a useful neural network approach for the solution of function approximation problems [4, 31, 40, 45, 62]. To determine the optimal distribution of membership functions, the ANFIS gives the mapping relation between the input and output data. ANFIS architecture consists of both artificial neural network (ANN) and fuzzy logic (FL). The system includes five layers. The node function describes several nodes, which are to be included in the ANFIS layer. The inputs of present layers are obtained from the previous layers. For example, consider two inputs (x, y) and one output (fi) are used in this system. The rule base contains fuzzy if-then rules. Thus, the two rules are:


where x and y are the inputs and A and B are the fuzzy sets fi (x, y). The feature extraction is done using DB2 wavelet filter, and the optimum features are obtained using GA [16]. Then the classification is done using GA with neural network and GA with ANFIS, and the results are shown in Figure 6. Based on the classified output, it is clearly understood that the GA and ANFIS shows the better classification.

every other neurons of previous layer. Softmax is the final layer and it calculates the probability value. The higher probability becomes the output. After training, the

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.

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

system will be able to classify the image automatically without human intervention. The classification is done for Vaihingen city and the results are

Classified output using DB2 with (a) GA and neural network and (b) GA and ANFIS.

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

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

4.4 Classification using multilayer perceptron layer

(a) Vaihingen city and (b) classified output of Vaihingen city.

4.5 Limitations of different methodologies

displayed in Figure 7.

Figure 7.

Figure 6.

4.5.1 Genetic algorithm

67

#### 4.3 Classification using CNN

The classification using CNN is done using the deep features obtained from the training phase of CNN [2, 5, 24]. In training, it carries out the predefined process with one or multiple layers. In a fully connected layer, every neuron is connected to


Table 2. Classification results of Madurai city for different wavelet packet transforms. Land Cover/Land Use Mapping Using Soft Computing Techniques with Optimized Features DOI: http://dx.doi.org/10.5772/intechopen.86218

#### Figure 6.

4.1 Classification based on Mahalanobis distance

Land Use Change and Sustainability

D2

• Rule 1: If x is A1 and y is B1, then z is f1(x, y).

• Rule 2: If x is A2 and y is B2, then z is f2(x, y).

4.3 Classification using CNN

Table 2.

66

In this method, the decomposition for test texture image is done using DWPD. In the same manner, another set of features are obtained and compared with the obtained feature values. The class of textures is represented as C, the mean signature of class C is represented with mc, and the Mahalanobis distance is given by

<sup>X</sup>ð Þ xi � mc (13)

ð Þ¼ xi,C ð Þ xi � mc

If the distance D(i) is smallest, then the test texture image is classified as ith texture [15, 21, 47]. Features are obtained using many wavelet filters, and it is followed using classification process [14, 28, 44]. The overall, user, producer, and kappa accuracy indices obtained for the different wavelet filters presented in Table 2 show that the DB2 wavelet filter gives superior results than the other wavelet filters. Thus, the DB2 wavelet filter will be more useful for land cover/land use mapping.

4.2 Classification based on adaptive neuro-fuzzy inference system (ANFIS)

The adaptive network-based fuzzy inference system (ANFIS) is a useful neural network approach for the solution of function approximation problems [4, 31, 40, 45, 62]. To determine the optimal distribution of membership functions, the ANFIS gives the mapping relation between the input and output data. ANFIS architecture consists of both artificial neural network (ANN) and fuzzy logic (FL). The system includes five layers. The node function describes several nodes, which are to be included in the ANFIS layer. The inputs of present layers are obtained from the previous layers. For example, consider two inputs (x, y) and one output (fi) are used in this system. The rule base contains fuzzy if-then rules. Thus, the two rules are:

where x and y are the inputs and A and B are the fuzzy sets fi (x, y). The feature extraction is done using DB2 wavelet filter, and the optimum features are obtained using GA [16]. Then the classification is done using GA with neural network and GA with ANFIS, and the results are shown in Figure 6. Based on the classified output, it

The classification using CNN is done using the deep features obtained from the training phase of CNN [2, 5, 24]. In training, it carries out the predefined process with one or multiple layers. In a fully connected layer, every neuron is connected to

DB2 87.60 82.02 89.57 0.82 Symlet 2 78.78 76.45 76.43 0.69 Coiflet 2 77.3 73.3 70.6 0.67 Bi-or 2.2 79.2 79.76 76.69 0.69

Classification results of Madurai city for different wavelet packet transforms.

Overall User Producer Kappa

is clearly understood that the GA and ANFIS shows the better classification.

Classified output using DB2 with (a) GA and neural network and (b) GA and ANFIS.

every other neurons of previous layer. Softmax is the final layer and it calculates the probability value. The higher probability becomes the output. After training, the system will be able to classify the image automatically without human intervention. The classification is done for Vaihingen city and the results are displayed in Figure 7.
