Rotation Invariant Sonar Image Segmentation for Undersea Cables

Undersea cable detection is a prerequisite for cable maintenance and repair. However, extracting cables from side-scan sonar images is challenging due to the lack of details and interference from seabed sediments. In this article, an automatic rotation-invariant segmentation method for undersea cables is proposed. First, a filter based on the curvelet transform is designed to extract features of cables automatically. Second, a 2-D constant false alarm rate detector is used for feature denoising. Third, a morphology repair method is proposed to fulfill features that have been missed during feature extraction and image denoising. Finally, the maximum connected area in images is retained for cable segmentation. Results show that the proposed method can extract cables accurately. Four performance indicators, including structural similarity index, precision, pixel accuracy, and intersection over union reach 0.9810, 0.6108, 0.8348, and 0.8915, respectively. Consistent performance has been observed in images with different cable postures.