Deep learning-based recognition of underwater target
Abstract
Underwater target recognition remains a challenging task due to the complex and changeable environment. There have been a huge number of methods to deal with this problem. However, most of them fail to hierarchically extract deep features. In this paper, a novel deep learning framework for underwater target classification is proposed. First, instead of extracting features relying on expert knowledge, sparse autoencoder (AE) is utilized to learn invariant features from the spectral data of underwater targets. Second, stacked autoencoder (SAE) is used to get high-level features as a deep learning method. At last, the joint of SAE and softmax is proposed to classify the underwater targets. Experiment results with the received signal data from three different targets on the sea indicated that the proposed approach can get the highest classification accuracy compared with support vector machine (SVM) and probabilistic neural network (PNN).
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The auditory-inspired CNN had a high accuracy (87.2%), outperforming a standard CNN (83.2%), a stack of autoencoders (81.4%), wavelet transform with PCA (74.6%), and a support-vector machine (68.2%).
“The results showed that the proposed auditory-inspired CNN had a high accuracy (87.2%), outperforming a standard CNN (83.2%), a stack of autoencoders [113] (81.4%) and various traditional object recognition algorithms, such as a combination of wavelet transform with PCA [114] (74.6%) and a support-vector machine [115] (68.2%).”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [113]
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- Authors
- Cao, X., Zhang, X., Yu, Y., & Niu, L.
- Affiliations
- Northwestern Polytechnical University (CN)
- Venue
- 2016 IEEE International Conference on Digital Signal Processing (DSP)
- Publisher
- IEEE
- Published
- 2016-10
- Volume
- 2016
- Pages
- 89-93
- OpenAlex
- W2594878708
- Cited by
- 78
- References
- 20
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 7 · 2023: 7 · 2022: 14 · 2021: 15 · 2020: 7 · 2019: 7 · 2018: 10 · 2017: 3
- FWCI
- 35.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Digital Signal Processing (DSP) (publishedVersion)
- IEEE Conference Proceedings
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- underwater target recognition, underwater target classification, sparse autoencoders, stacked autoencoder, deep feature extraction, invariant feature learning, underwater acoustic signals, spectral data, softmax classification
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