Canonical Coordinates for Detection and Classification of Underwater Objects From Sonar Imagery

Detection and classification of underwater objects in sonar imagery are challenging problems. In this paper, a new coherent-based method for detecting and classifying potential targets in high-resolution sonar imagery is developed using canonical correlation analysis (CCA). Canonical coordinate decomposition allows us to quantify the changes between the returns from the bottom and any target activity in sonar images and at the same time extract useful features for subsequent classification without the need to perform separate detection and feature extraction. Moreover, in situations where any visual analysis or verification by human operators is required, the detected/classified objects can be reconstructed from the coherent features. In this paper, underwater target detection and classification using the canonical correlations extracted from regions of interest within the sonar image is considered. Test results of the proposed method on underwater side-scan sonar images provided by the Naval Surface Warfare Center (NSWC) in Panama City, Florida, is presented. This database contains synthesized targets in real background varying in degree of difficulty and bottom clutter. Results illustrating the effectiveness of the CCA-based detection and classification method are presented for various densities of background clutter and bottom difficulty.