A Study Of Sonar Image Processing And Target Recognition

Target recognition based on image processing method in sonar image is a hot and difficult issue in sonar signal processing. With the development of some imaging sonar system, target recognition can be performed not only in one dimension but also in two or three dimensions. More and more scholars and engineers pay attention to the imaging sonar system. This dissertation applied some methods of image processing and pattern recognition to solve the target recognition issue of sonar images. It includes the whole aspects in image recognition processing algorithms such as denoising, image pre-processing, image segmentation, target detection and classification.Firstly, this dissertation lucubrated the image denoising method which is the key step of image pre-processing. Two denoising methods were adopted in the dissertation, namely wavelet denoising and total variation denoising based on Partial Differential Equation (PDE). The result indicated that these two methods could preserve the image features and the method based on PDE with higher SNR performed better than wavelet denoising.Two methods were proposed in this dissertation to solve the problem of target detection. One is named Mask Matching, which made use of the characteristics of sonar image. The other is Fractal Filtering, which could classify natural targets from artificial targets. Utilizing their individual characteristics and advantages we could detect suspicious region easily.The next step is mapping the images to feature space. Through extracting features of suspicious region and constructing feature vectors we can carry out the task. According to the physical trait of sonar image, we extracted some statistic features of sonar image. Furthermore we studied the moment invariants to fit the diversification of translation invariants, scale invariants, and rotation invariants of target.Additionally the dissertation studied the RBF neutral network and applied it to classify target of sonar image. Compared with BP network, it showed that RBF neutral network has some advantages of designing classifier and its simulating result is satisfying.