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

Content Provider	IEEE Xplore Digital Library
Author	Tucker, J.D. Azimi-Sadjadi, M.R. Dobeck, G.J.
Copyright Year	2007
Description	Author affiliation: Colorado State Univ., Fort Collins (Tucker, J.D.; Azimi-Sadjadi, M.R.)
Abstract	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.
Starting Page	1
Ending Page	6
File Size	2779682
Page Count	6
File Format	PDF
ISBN	9781424406340
DOI	10.1109/OCEANSE.2007.4302270
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-18
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sonar detection Object detection Underwater tracking Feature extraction Image analysis Humans Image reconstruction Testing Surface reconstruction Cities and towns
Content Type	Text
Resource Type	Article

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