Semi-Automatic Identification of Submarine Pipelines with Synthetic Aperture Sonar Images

Abstract

Synthetic Aperture Sonar (SAS) is a sensor that was designed for hydrographic survey of the seabed. It detects small targets, enabling high geometric resolution images. However, the images generated by SAS are susceptible to speckle noise, which makes digital processing difficult, since the noises are confused with the targets of interest. The goal of this study was to develop a semi-automatic routine for SAS image processing to verify the structural integrity of submarine pipelines. The method presented incorporates four stages: pre-processing to reduce noise and highlight the targets of interest; extraction of features aiming to recognize features related to the pipelines; post-processing to reduce the fragmentation generated during feature extraction; validation to quantify the results from the reference images to estimate the performance of the proposed methodology. The results showed that more than 80% of the submarine pipelines were mapped in the semi-automatic mode, which considerably reduced the time needed to manually identify a large number of pipelines operating on offshore oilfields.

Keywords:

• Autonomous underwater vehicle (AUV)
• features extraction
• submarine pipelines
• synthetic aperture sonar (SAS)

Acknowledgment

The authors would like to thank Petrobras S.A. and its engineers André Luiz Neves de Souza and Marksuel Xavier Bastos for encouraging and providing the SAS images of this research.