IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 11, Issue 6, Ver. III (Nov.-Dec .2016), PP 46-52 www.iosrjournals.org DOI: 10.9790/2834-1106034652 www.iosrjournals.org 46 | Page Shallow metal object Detection at X-Band using ANN and Image analysis Techniques Patri Upender 1 , K.R.Anudeep Laxmikanth 2 (Department of Electronics and Communication Engineering, Vignan Institute of technology & science, India) 1 (Department of Electronics and Communication Engineering, Vignan Institute of technology & science, India) 2 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 (10.0GHz). 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. Keywords: Shallow buried objects, image analysis, monostatic scatterometer, ANN, X-band, horn antenna. I. Introduction A lot of researchers from various fields (like archaeology, criminology, military, geophysical exploration, submarine detection etc.) are involved in the detection of buried objects. Microwave radar is known to be the most likely answer to these detection problems except submarine detection where acoustic detection finds maximum use [1–3]. Brunzell [4] describes use of pulse radar for detecting buried object. In subsurface detection, when exploring with X-band of microwave, the nearest target is usually at a distance of about 1meter, maintaining the sanctity of the far field antenna. Hence pulse radar must have extremely high precision in time domain, making it quite expensive. CW radar with low frequency modulation can be used for subsurface detection, keeping system cost low. However CW radar does not provide range information. FM-CW radar is a cheaper alternative to pulse radar, if information about depth of object is needed. Yamaguchi [5–7] described the use of FM-CW radar for detecting human body buried in wet snow pack. Carin [8] described the use of polarimetric synthetic aperture radar (SAR) radar for detecting landmines. All the above mentioned investigation mainly concentrated on detecting buried objects. Considering the problem of detecting landmines with the help of data obtained by radar, there are possibilities of a large number of false alarms due to stones, tree roots etc. A typical war field will usually contain many metal fragments. These objects will interfere with the detection of landmines. Some method is needed which will classify the detected objects. Various image processing techniques have been found extensive use and have increased the confidence in detection of the shape of object, and hence classify them. However these techniques are found to be ineffective when backscattered signal quality is poor (i.e., includes noise with surroundings) [1–8]. Use of neural networks [9] while signal processing the data collected by various remote sensing systems is increasing day by day. Yoshida [10] proposed a pattern classification method for remote sensing data using neural networks on problem of land cover mapping. Tsintikidis [11] demonstrated the potential of neural networks for radiometric sensing of land surface parameters. Bischof [12] demonstrated usefulness of neural networks on problem of multispectral land-sat image classification. The unique ability of human brain to recognize objects under poor observable conditions motivated us to apply neural networks to the problem of recognizing an object buried beneath ground using CW radar. Neural networks offer parallel distributed computing platform that does not need programming like conventional computers. Instead neural networks learn from sample examples and due to generalization property, they are able to correctly solve instances of problems not used during learning. The complexity of the problem increases because of the lossy nature of the medium between air and the dielectric object under consideration. The precise detection of land mines, unexploded ordnances, plastic pipes etc. are some of the major challenges to the researchers. Since mechanical probing of soil is not possible in every case and is impractical in some of the cases. That’s why the importance of GPR (Ground Penetrating Radar) has greatly increased. But the distance from the object is limitation of GPR. Therefore, it is important to develop some techniques based on remote sensing by which shape of these types of buried object can be recognized with air-borne or space borne sensors. For this purpose, microwave remote sensing can be used as a powerful tool. Therefore, in this paper an