1. Introduction

The remote sensing (RS) image has millions and millions of details hidden into it. The interpretation of RS images thus leads to a variety of new improvements in our daily life. Since RS image coils a lot of areas in a single image, intensive care has to be taken while handling each and every pixel [25, 41, 42, 61]. Also, extraction of features plays an important role. Using those features, a particular pixel can be classified easily [32, 33, 46, 55, 56]. Deciding which features we are going to extract is important, and it has to be done based on the application and type of image.

The classified output has several uses in civil engineering. It is also useful in planning for large airports, industrial estates, and harbors and the construction of dams, bridges, and pipelines. It also provides valuable data for the process and design of roads and highways. The application areas also extend to extracting building footprints, detecting roads, and outlining urban changes from a pair of images taken at different dates. It also extends to the field of forest investigation, water management, and disaster management.

Similarly, the interpretation of RS images has many applications [34]. They include the study of forest where investigating the landscape of forest area can avoid deforestation and degradation processes. Forest land cover describes the physiographical characteristics of the environment from bare rock to tropical forest. So, classifying these will result in the understanding of the variety and type of land cover. Another important advantage with forest land cover is identification of very specific habitats and distribution of both individual species and species assemblies. In the case of urban planning, the year-wise RS images are analyzed to find whether the occupation is growing in the right place. While planning the urban area utilization, the government may plan with the RS image, so that the road construction plan, water pipeline construction plan, and power supply connection plan can be made easy. If in case our urban occupation is happening in the vegetation area, then it should be taken care of and constructions are to be made in other areas.

The mother wavelet y has to satisfy the admissibility criterion to ensure that it is a localized zero mean function [39]. Typically, some more constraints are imposed on y to ensure that the transform is non-redundant and complete and constitutes a multi-resolution representation of the original signal. This results in a good realspace transform implementation using quadrature mirror filters. The convolution is performed, and the results with the low-pass filter are called approximation image, and the results with the high-pass filter in specific directions are called detail images. In earlier processes, the image is split into an approximation and detail images. The approximation is then split itself into a second level of approximation and details. For a n-level, the signal decomposition can be represented using Eq. (3):

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

 ↓2, <sup>1</sup> ↓1, <sup>2</sup>

 ↓2, <sup>1</sup> ↓1, <sup>2</sup>

 ↓2, <sup>1</sup> ↓1, <sup>2</sup> (2)

 ↓2, <sup>1</sup> ↓1, <sup>2</sup>

where "\*" denotes the convolution operator, "↓2,1" denotes the downsampling along the rows (columns), A0 = I is the original image, and H and G are low-pass and high-pass filters, respectively. I(x, y) is the original image. An is obtained by low-pass filtering and is the approximation image at scale n. The detail images Dni are obtained by band-pass filtering in a specific direction (i = 1, 2, 3 for vertical, horizontal, and diagonal directions, respectively) and thus contain directional detail information at scale n. The original image, I, is thus represented by a set of sub-

The wavelet packet decomposition offers a richer signal analysis. Here, the split happens for both detail image and approximation image. This results in a wavelet decomposition tree. The details present in detail images are helpful in analyzing texture and discrimination. To characterize a texture, the features derived from detail images are used. The following section discusses the way in which the fea-

The filter choice and its order may vary for each application. Here, two levels of wavelet packet decomposition with different wavelet families are done and shown in Figure 1. There is no need to perform a deeper decomposition because, after the second level, the size of images become too small and no more valuable information is obtained. Sixteen wavelet coefficient matrices containing texture information are

In texture training, the known texture images are decomposed using DWPD. To create feature database, a set of WPSF, such as mean and standard deviation, is

An ¼ Hx ∗ Hy ∗ An�<sup>1</sup>

Dn1 ¼ Hx ∗ Gy ∗ An�<sup>1</sup>

Dn2 ¼ Gx ∗ Hy ∗ An�<sup>1</sup>

Dn3 ¼ Gx ∗ Gy ∗ An�<sup>1</sup>

tures from wavelet transformed image to be used for classification.

produced from the second level of decomposition.

images at several scales: {An, Dni}.

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

Figure 1.

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A wavelet packet tree.

RS images are also used in water management system to clearly display sediment pollution and oil spills over water bodies and help to monitor the quality of water resources. They are also used in disaster management. In case of natural disaster, risk-prone areas are detected, and risk management is undertaken. When sudden natural disaster happens, it is difficult for humans to collect data at that moment, and so using RS technology, we can handle the situation.

The application area also covers the hazard management. As water-related natural hazards occur due to a number of factors, such as structure, drainage, slope, land use, road network, etc., they must be taken into account when assessing the region's instability and potential hazard risks. It is essential because proper hazard management can help us take timely measures to prevent flooding and following landslides.

The chapter is organized in the following way. Section 2 explains the feature extraction process, Section 3 explains the feature subset selection, Section 4 explains about feature classification, and Section 5 concludes the chapter.
