kumoh national institute of technology
Networked Systems Lab.

V.S. Doan, T. Huynh-The, D. S. Kim, "Underwater Acoustic Target Classification based on Dense Convolutional Neural Network", DOI: 10.1109/LGRS.2020.3029584, IEEE Geoscience and Remote Sensing Letters, 2020 (A)
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Date : 2020-10-03
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In oceanic remote sensing operations, underwater acoustic target recognition is always a difficult and extremely important task of sonar systems, especially in the condition of complex sound wave propagation characteristics. The expensively learning recognition model for big data analysis is typically an obstacle for most traditional machine learning (ML) algorithms, whereas the convolutional neural network (CNN), a type of deep neural network, can automatically extract features for accurate classification. In this study, we propose an approach using a dense CNN model for underwater target recognition. The network architecture is designed to cleverly reuse all former feature maps to optimize classification rates under various impaired conditions while satisfying low computational cost. In addition, instead of using time-frequency spectrogram images, the proposed scheme allows directly utilizing the original audio