Side-scan sonar imagery segmentation based on Markov random field model

Side-scan sonar image(SSI) must be segmented into regions of shadow,sea-bottom-reverberation,and object-highlight before underwater object can automatically be detected and recognized.Because strong background noises exist,traditional algorithms of image segmenting are useless.The algorithm based on Markov random field model is introduced.The segmentation can be constrained by the aprior information,according to the characteristics of object on the SSI.Furthermore,it is highlight intensity in an object area and lowlight intensity in a shadow area,so the ratio of shadow intensity to object intensity is very small.The SSI can be initially segmented by the three aprior information.After the initial segmentation has been completed,a false objects can be detected through the characteristic that the difference between the widths of object and shadow is close to one.And then,an MRF model parameter can be solved with the least square,and an noise parameter can be calculated with the maximum likelihood approach.Finally,the segmentation can be accomplished with the ICE method.The MRF model provides a reliable method for obtaining this underlying label field through incorporating pixel dependencies into the segmentation model.This is rational and robust.It has few influences when strong speckle noise exists.This fine result is obtained through the real SSI.