IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Processing and analysis of ground penetrating radar landmine detection
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Infrared land mine detection by parametric modeling
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
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The ground penetrating radar (GPR) and infrared (IR) imaging have become two established sensors for detecting buried anti-personnel mines (APM) which contains no or a little metal. This paper introduces the GPR and IR techniques briefly and describes particular situations where each technique is feasible. We discuss the GPR and IR data acquisition, signal processing and image processing methods. This paper discusses the strengths and weaknesses of each of the sensors based on data capturing efficiency, overcoming environmental difficulties and sensor technology. By providing comparison of these two sensors, we emphasise the necessity of fusion to harness the advantages of each of the methods. We propose a geometrical feature-based sensor fusion framework, combining GPR and IR, as an effective technique for detection and classification of APM which reduces the false alarm rate significantly. We consider the basic geometrical shape descriptor features of an object and construct a feature vector for each of the objects. These feature vectors are used to train a probabilistic neural network (PNN) for the classification of APMs. The method gives almost perfect detection accuracy.