Transfer Learning with SAS-Image Convolutional Neural Networks for Improved Underwater Target Classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

The value of transferring convolutional neural networks (CNNs) trained with synthetic aperture sonar (SAS) imagery is demonstrated in the context of an underwater unexploded ordnance (UXO) classification task. Specifically, it is shown that CNNs designed for, and trained on, a mine classification task can be transferred across sensors of the same modality - but different frequency bands and sensor resolutions - and also across target concept (from mines to UXO). Importantly, it is shown that this transfer learning outperforms simply training the CNNs "from scratch" using the limited available data that pertains to the ultimate task. A key element underlying this approach is that the CNNs be specially tailored to the particularities of the sensor modality and its data. These findings are valuable because they illustrate how training-data requirements can be eased for data-limited remote-sensing applications.

Original languageEnglish (US)
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages78-81
Number of pages4
ISBN (Electronic)9781538691540
DOIs
StatePublished - Jul 2019
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: Jul 28 2019Aug 2 2019

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/TerritoryJapan
CityYokohama
Period7/28/198/2/19

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences

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