Amplitude-based dominant component analysis for underwater mines extraction in side scans sonar

摘要

For the first time, the application of the amplitude dominant component analysis (ADCA) to the segmentation of sonar images is explored. We exploit the saliency of the objects in side scans sonar images for underwater mines recognition. Due to the textural and multicomponent nature of the sonar image, a set of bandpass filters is used to decompose the image into narrowband components which lends itself more easily to analysis. The filters bank used is a set of Gabor filters, favored due to their optimal joint spatial and spectral localization. The ADCA-based segmentation is illustrated on real high-resolution sonar images, yielding very promising results showing the interest to exploit the saliency of sonar images.