Year: 2017
Type: conference-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.
Source: Wavelets and Sparsity
DOI: 10.1117/12.2272497
Authors: Naoki Saito, David Weber
Institution: University of California, Davis
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2964097992