The Method Of Landmine Image Processing And Object Recognizing Based On Ground Penetrating Radar

Landmine image processing and analysis are very important in the landmine system. Traditional metal detector works well in metal landmine detection, but it is not useful to the non-metal or minimum-metal landmines, such as plastic landmines. In addition, this kind of landmines is mostly harmful after World War â…¡. The new detector-ground penetrating radar and the image processing of ground penetrating radar are needed. Though there are many countries which pay attention to it with many people and lot of money, the result is not satisfied.The main contribution in this thesis is as follows. Firstly a new method of recognition of landmines is put forward by using the support vector machine. Then the competition learning algorithm is improved to obtain the landmine image segmentation. Furthermore, a new method of registration is given for image fusion. Finally, all these methods are put into an image model.The detail researches are1. Landmines recognition by support vector machine. The ground penetrating radar image of landmine has its own characteristic that it is formed by three hyperbola, the others are one or two hyperbola. From this point of view, a new method of landmine recognition is put forward by support vector machine.2. Image segment is a primary step in image analysis of landmines detection by ground penetrating radar sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this thesis a new theory brought forward, that is, I look the weight sets as target vector sets which are the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and closes to the real size of target.3. Landmine image registration approach by metal sensor and ground penetrating radar sensor is presented. I go forward an approach which is based oncompetition learning and support vector machine. Firstly, I describe the feature of landmine image by the weights of competition learning in order to decrease the vector numbers of being processed and pay attention to the interesting region. Secondly, put the weights into support vector machine as training vectors, then to obtain the support vectors in a cycle way. I demonstrate the efficiency of this approach by applying this method to three group of registration and fusion of metal sensor images and ground penetrating radar sensor images. The results show that the approach is feasible and would be the base for further image processing of landmine.4. On the base of the analysis of landmine image processing and object recognizing, a landmine image processing and object recognizing model is proposed.Methods here are especially for landmine image processing which are all according to the feathers of landmine and ground penetrating radar detector. They would be useful to the theory and method of landmine detection system.