Multi-aspect detection and classification of buried underwater objects using the new Buried Object Scanning Sonar (BOSS) data is the main goal of this project. Canonical coordinate decomposition (CCD) was applied to extract the most coherent features of the buried or bottom objects in two sonar pings with a certain separation. CCD provides an elegant framework for analyzing linear dependence and mutual information between two data channels. These features are then used for subsequent classification. For this study, single-aspect and multi-aspect classification schemes are evaluated, and the results presented in terms of confusion matrices. Additionally, the results of applying both the single and multi-aspect classifiers to the entire test runs are presented to show the real usefulness of the algorithms for buried/bottom mine-hunting.

In this paper, a new collaborative multi-aspect classification system (CMAC) is introduced. CMAC utilizes a group of collaborative decision-making agents capable of producing a high-confidence final decision based on features obtained over multiple aspects. This system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multi-ping classification of mine-like objects while simultaneously reducing the number of false alarms compared to a multi-ping decision-level fusion classifier.

Abstract-In this paper, a new collaborative multiaspect classification system (CMAC) is introduced, which utilizes a group of collaborative decision-making agents capable of producing a highconfidence final decision based on features obtained over multiple aspects. It is also shown how CMAC can be modified to perform multiaspect classification using a decision feedback (DF) strategy. The system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multiple-ping classification of mine-like objects while reducing the number of false alarms compared to other multiple-ping classification fusion systems such as nonlinear decision-level fusion (DLF).

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Abstract-Multichannel Canonical Correlation Analysis (MCCA) is used in this paper for feature extraction from multiple sonar returns off of buried underwater objects using data collected by the new generation Buried Object Scanning Sonar (BOSS) system. Comparisons are made between the classification results of features extracted by the proposed algorithm and those extracted by the two-channel Canonical Correlation Analysis (CCA) algorithm. This study compares different feature extraction and classification algorithms, and the results are presented in terms of confusion matrices. The results show that MCCA yields higher correct classification rates than CCA while reducing the classifier's structural complexity.