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Proceedings Paper

Underwater object classification using scattering transform of sonar signals
Author(s): Naoki Saito; David S. Weber
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Paper Abstract

In this paper, we apply the scattering transform (ST)—a nonlinear map based off of a convolutional neural network (CNN)—to classification of underwater objects using sonar signals. The ST formalizes the observation that the filters learned by a CNN have wavelet-like structure. We achieve effective binary classification both on a real dataset of Unexploded Ordinance (UXOs), as well as synthetically generated examples. We also explore the effects on the waveforms with respect to changes in the object domain (e.g., translation, rotation, and acoustic impedance, etc.), and examine the consequences coming from theoretical results for the scattering transform. We show that the scattering transform is capable of excellent classification on both the synthetic and real problems, thanks to having more quasi-invariance properties that are well-suited to translation and rotation of the object.

Paper Details

Date Published: 24 August 2017
PDF: 13 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940K (24 August 2017); doi: 10.1117/12.2272497
Show Author Affiliations
Naoki Saito, Univ. of California, Davis (United States)
David S. Weber, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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