SHAPE RECOGNITION OF SHALLOW BURIED METALLIC OBJECTS AT X-BAND USING ANN AND IMAGE ANALYSIS TECHNIQUES

Abstract

A robust algorithm has been developed for improving the backscattered signal and recognizing the shape of the shallow buried metallic object using Artificial Neural Network (ANN) and image analysis techniques for remote sensing at X-band. An ANN with image analysis technique based on tangent analysis is proposed to recognize the shape of metallic buried objects and minimize the orientation effect of buried object. The experimental setup has been assembled for detecting the buried metallic objects of any size at different depths in the sand pit. The system uses only one pyramidal horn antenna for transmitting and receiving microwave signals at X-band (10.0 GHz). All the data to be processed by this algorithm has been received by moving the transmitter/receiver to different locations at a single frequency in X-band in the far field region. ANN technique has been found to be very efficient. An effective training technique has been used to improve the effectiveness of the algorithm. The retrieved result of shape is in good agreement with original shape.