Analyzing Transfer Learning Methods For UXO Classification In Varying Shallow Water Environments

This paper investigates and compares two different transfer learning methods for the purpose of classifying underwater objects in sonar data from different environments and operating conditions. The popular efficient lifelong learning algorithm (ELLA) is used to perform this classification task. Two...

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Bibliographic Details
Published in2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) pp. 1 - 6
Main Authors Larson, Nathan, Hall, Jack, Azimi-Sadjadi, M. R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Dictionaries
Dictionary Learning
Incremental Learning
Machine learning
Matching pursuit algorithms
Multi-task Learning
Sonar
Sparse Coding
Sparse matrices
Task analysis
Training
Unexploded Ordnance (UXO) Classification
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Summary:This paper investigates and compares two different transfer learning methods for the purpose of classifying underwater objects in sonar data from different environments and operating conditions. The popular efficient lifelong learning algorithm (ELLA) is used to perform this classification task. Two different learning strategies are then proposed for ELLA. As a benchmark the Matched Subspace Classifier (MSC) was also used together with an incremental dictionary learning and sparse coding. The comparison is carried out on low frequency sonar spectral features extracted from different underwater unexploded ordnances (UXOs) and non-UXO objects in two different environmental conditions.
DOI:10.1109/MLSP.2019.8918688