Object representation for multi-beam sonar image using local higher-order statistics

Abstract Multi-beam sonar imaging has been widely used in various underwater tasks such as object recognition and object tracking. Problems remain, however, when the sonar images are characterized by low signal-to-noise ratio, low resolution, and amplitude alterations due to viewpoint changes. This paper investigates the capacity of local higher-order statistics (HOS) to represent objects in multi-beam sonar images. The Weibull distribution has been used for modeling the background of the image. Local HOS involving skewness is estimated using a sliding computational window, thus generating the local skewness image of which a square structure is associated with a potential object. The ability of object representation with different signal-to-noise ratio (SNR) between object and background is analyzed, and the choice of the computational window size is discussed. In the case of the object with high SNR, a novel algorithm based on background estimation is proposed to reduce side lobe and retain object regions. The performance of object representation has been evaluated using real data that provided encouraging results in the case of the object with low amplitude, high side lobes, or large fluctuant amplitude. In conclusion, local HOS provides more reliable and stable information relating to the potential object and improves the object representation in multi-beam sonar image