Research On The Side-scan Sonar Image Segmentation Algorithm

Since the birth of the side-scan sonar, it has been widely used in civil and military fields.With the rapid development of computer technology and the digitization of side-scan sonar,side-scan sonar image target automatic segmentation and self-recognition technology arepromoting the development of intelligent underwater devices. For the reason of complexunderwater environment and other factors, sides-can sonar has some defaults such as imagewith serious noise pollution and poor contrast ratio. Researchers process scan-sonar imagesby using various pretreatment methods and segmentation algorithm to improve the results,and have achieved certain results, but there is still not a mature approach yet. In differentactual side-scan sonar image segmentation applications, the requirements of accuracy,adaptability and speed degree are different. This article focuses on in-depth study of fuzzyclustering algorithm and level set algorithm improved method to improve the accuracy andadaptability. The improved hierarchical Markov model segmentation algorithm and thespecific target segmentation algorithm using auxiliary information, which can quickly andaccurately get the segmentation images, are proposed. After reading a lot of literature andanalyzing domestic and international status, the main work of this paper is determined asfollows:(1) Research on side-scan sonar image preprocessing methods. After decoding side-scansonar data, on the basis of the original data, the heading angle optimization model andcorrection algorithm are proposed. The images are geometrically corrected, and the imagecoordinate and the Earth coordinate conversion rules are proposed. A study on the gradationcorrection method is done to further improve image quality. Side-scan sonar image texturedescription method is introduced, and GMRF texture image and Gabor texture image areextracted. After researching on side-scan sonar image filtering algorithm, an improved BEMDimage filtering algorithm is proposed. They are basis of following segmentation work.(2) Research on side-scan sonar image clustering segmentation algorithm. Scan sonarimages are processed using the several common segmentation algorithms, finding out theinadequacies and insufficiencies, and improving the algorithm by using texture features. Afteranalyzing the initial cluster centers selected rules, the membership function is rewritten toobtain certain results. Then combined with the improved BEMD filtering method, thealgorithm is improved. Experiment results have proved that the algorithm has stableperformance of the segmentation and has a strong ability to adapt to different pictures.(3) Research on level set algorithms of side-scan sonar image segmentation. After the basic model of the CV model, four-phase level set model and hierarchical level set model areintroduced. The segmentation experiments are done using these models. On this basis, aftercollecting image texture information, further studies of level set models driven by GMRFtextures energy and Gabor texture energy are done. Through the analysis of the shortcomingsof these models, improved four-phase level set segmentation model and rapid hierarchicallevel set model are proposed. These models are able to obtain better segmentation results, andhigher segmentation speed.(4) The quickly hierarchical MRF side-scan sonar image segmentation algorithm isproposed. Plane MRF model and hierarchical MRF model are introduced. Through makingthe target area and shaded areas in a class, the MRF model parameters are reduced. As the useof the grayscale statistical methods, the calculation of the parameter estimates described bygray distribution model is reduced. Segmentation experiments verify that the algorithm is fastand effective. In order to further improve the segmentation results and the accuracy ofsegmentation, database of experts assisted segmentation algorithm is proposed. It can obtaingood results.To be continued, the application of the algorithms is described and the specific targetsegmentation algorithm is proposed. Regional inspection method is used to detect isolatedtargets in sonar images. After analyzing the geometric space characteristics of particularshapes, the regional judge method is proposed.(5) The full text of the innovation and research are summarized, and the researchdirection of the next step is described. Combined with previous content, specific size targetsegmentation method is described. Finally, a summary of innovation and research result inthis paper is provided, and the problems needing to be solved and future works needing to bedone are described also.