Underwater object classification using scattering transform of sonar signals

Naoki Saito; David S. Weber

doi:10.1117/12.2272497

24 August 2017 Underwater object classification using scattering transform of sonar signals

Naoki Saito, David S. Weber

Proceedings Volume 10394, Wavelets and Sparsity XVII; 103940K (2017) https://doi.org/10.1117/12.2272497
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Naoki Saito and David S. Weber "Underwater object classification using scattering transform of sonar signals", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940K (24 August 2017); https://doi.org/10.1117/12.2272497

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