目的 针对侧扫声纳图像具有背景复杂、噪声污染重等特点,提出一种结合空间信息的模糊聚类分割算法(FCM),以提高侧扫声纳图像的分割精度和分割速度。方法 采用结合组合邻域中值滤波的FCM算法,首先选取正十字邻域和斜十字邻域,分别计算两个邻域内的像素灰度中值;然后,结合其中的较小值,引入惩罚项,得到融合灰度信息和空间信息的灰度值;最后,对融合后的灰度图像进行FCM分割。结果 利用该算法对不同尺寸和不同目标的侧扫声纳图像进行分割,并将分割结果与其他5种聚类算法的分割结果进行比较,对比分析每种算法的分割精度和运算时间。结合中值滤波的FCM算法的分割精度和运算时间均优于传统的FCM算法和结合均值滤波的FCM算法,其中结合组合邻域中值滤波的FCM算法的运算速度较快,分割精度略高于结合传统中值滤波的FCM算法。结论 结合组合邻域中值滤波的FCM算法在对侧扫声纳图像进行分割时,具有较强的抗噪性、实时性和较强的边缘保持能力。

Objective 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. Method 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. Result 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. Result 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. Conclusion 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.