Side-scan sonar has been widely used in several tasks

such as underwater target detection

tracking of undersea pipeline

and marine investigations. Side-scan sonar image usually contains complex background and serious noise pollution. With regard to these features

an image segmentation method via fuzzy clustering with spatial contextual information is presented to improve segmentation accuracy and operation time. In this study

we selected the combination neighborhood median filter as spatial contextual information. The basic idea of the presented method is as follows. First

we selected a cross-shaped neighborhood and an oblique cross-shaped neighborhood

and medians were calculated in the neighborhood. By introducing a penalty term

we then chose small median to obtain gray level

which integrates gray information and spatial contextual information. Finally

we applied the fuzzy C-means (FCM) clustering method on the gray image that integrated two kinds of information. To prove that the presented method has higher segmentation accuracy and shorter operation time than other fuzzy clustering methods

we compared it with other five kinds of fuzzy clustering method

which include FCM

bias-corrected FCM

FCM_S1

FCM_S2

and FCM combined with traditional square neighborhood median filter. shows that the presented method has higher segmentation accuracy

shorter operation time

and stronger performance of keeping edge information than the other methods because of its combination with neighborhood median filter. By comparing with the traditional FCM and other improved FCMs

the presented method combined with mean filter and traditional square neighborhood median filter can segment side-scan sonar image quickly and effectively and has strong antinoise performance

robustness

and real-time performance.