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Chapter 5

Abstract

a hypothetical case study.

feature classification

1. Introduction

59

Land Cover/Land Use Mapping

with Optimized Features

Selvaraj Rajesh and Gladima Nisia T.

Using Soft Computing Techniques

The chapter discusses soft computing techniques for solving complex computational tasks. It highlights some of the soft computing techniques like fuzzy logic, genetic algorithm, artificial neural network, and machine learning. The classification of the remotely sensed images is always a tedious task. So, here we explain how these soft computing techniques could be used for image classification. Image classification mainly concentrates on the feature's extraction process. The features extracted in an efficient manner improve classification accuracy. Hence, the different kinds of features and different methods for these extractions are explained. The best extracted features are selected using genetic algorithm. Various algorithms

are shown and comparisons are made. Finally, the results are verified using

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,

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.

Keywords: land cover/land use mapping, soft computing techniques, feature extraction, artificial neural network, wavelet transforms,

water management, and disaster management.
