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AUTOMATIC TARGET DETECTION IN SIDE-SCAN SONAR IMAGERY, USING FEATURE BASED ACOUSTIC CLASSIFICATION AND INDEPENDENT COMPONENT ANALYSIS

In the present paper a methodological scheme bringing together common Acoustic Classification Systems (ACS) and a promising data decomposition approach called Independent Component Analysis (ICA), is demonstrated regarding its suitability for detecting small targets in SSS imageries. Usually in the first stages of an ACS, a large number of features are extracted from distinct windows throughout the whole image and their dimensionality is reduced by common data decomposition techniques, such as Principal Component Analysis (PCA), prior to unsupervised classification. However, in the target detection issue, data decomposition should point towards finding components that represent sub-ordinary image anomalies (i.e. small targets) rather than dominant information. ICA has proven to be a suitable model for separating targets from background and this study represents a demonstration of its applicability regarding SideScan Sonar (SSS) images. The present study attempts to build a fully automated target detection approach that combines feature extraction, ICA and unsupervised classification techniques. The suitability of the proposed approach has been validated by comparing its performance to the equivalent of common ACS approaches, using a large data-set of ground-truthed targets in real SSS imagery. The method exhibited unquestionable superiority indicating that ICA may be worth further investigation by ATR system researchers and developers.