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An Improved Deep Canonical Correlation Fusion Method for Underwater Multisource Data
In complex underwater environments, the single mode of a single sensor cannot meet the precision requirement of object identification, and multisource fusion is currently the mainstream research approach. Deep canonical correlation analysis is an efficient feature fusion method but suffers from problems such as not strong scalability and low efficiency. Therefore, an improved deep canonical correlation analysis fusion method is proposed for underwater multisource sensor data containing noise.doi:10.1109/access.2020.3014495 fatcat:fna3pbrf5vb2llqjey3ul34xtu