Classification of Buried Objects Using Acoustic Waves

There is no doubt that the problem of land mines is one of the most important problems that concerns the whole world. Egypt is one of the countries that suffer from this problem. There are more than 23 million land mines that are subject to explode at any time and these land mines occupy a large area estimated by 3910 square kilometers. The source of these land mines is the wars; World War II and the Arab-Israeli wars of 1956, 1967, and 1973 in the eastern desert and Sinai. The problem of land mines is not human casualties only, but there are also serious economic losses. There are several obstacles that are faced in removing the land mines such as the loss or absence of maps as well as the high costs needed to remove them. The work presented in this thesis provides an introduction to land mines; definition, their components and types, a summary of the techniques and the different methods used for detecting and clearing them, and the operating principles of each method. This thesis proposes efficient land mine identification techniques, which help in identifying the several types of land mines with different dimensions, shapes, and types. These techniques use Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) and they are based on the MFCCs to identify the different types of land mines. The performance of these techniques is evaluated in the presence of different types of noise with and without blurring. The thesis also proposes a classification technique using ANNs to classify the different types of land mines into different categories based on MFCCs features.