Details
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (electronic) | 9781538627259 |
| Publication status | Published - 8 Feb 2018 |
| Event | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States Duration: 27 Nov 2017 → 1 Dec 2017 |
Abstract
Spatially high-resolution information on the seabed sediment is import for many applications in the fields of oceanic engineering, coastal engineering, habitat mapping, and others. The seabed sediment is typically described by information based on the grain-size distribution, which are derived from sediment samples collected from the seafloor. For covering large areas side-scan sonar systems are typically used, which measure the backscatter intensity. From this information the sediment types can be derived. We propose a model for the automatic sediment type classification of the side-scan sonar data, which is based on convolutional neural networks (CNN). A big advantage of CNN is that they provide an end-to-end training: the CNN derives appropriate features automatically during the training process, which are then used for classification. The approach is based on a patch-wise classification using ensemble voting. The approach is evaluated on real world side-scan sonar data, which have been labelled using four classes (fine, sand, coarse, and mixed sediment) by experts. While the prediction of sand achieves an accuracy of 83 percent, the accuracy for fine sediment is very poor (11 percent).
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
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2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Seabed sediment classification of side-scan sonar data using convolutional neural networks
AU - Berthold, Tim
AU - Leichter, Artem
AU - Rosenhahn, Bodo
AU - Berkhahn, Volker
AU - Valerius, Jennifer
N1 - Funding information: 1The Bundesamt für Seeschifffahrt und Hydrographie (BSH, Federal Maritime and Hydrographic Agency) is a higher federal authority in Germany coming under the jurisdication of the Federal Ministry of Transport and Digital Infrastructure.
PY - 2018/2/8
Y1 - 2018/2/8
N2 - Spatially high-resolution information on the seabed sediment is import for many applications in the fields of oceanic engineering, coastal engineering, habitat mapping, and others. The seabed sediment is typically described by information based on the grain-size distribution, which are derived from sediment samples collected from the seafloor. For covering large areas side-scan sonar systems are typically used, which measure the backscatter intensity. From this information the sediment types can be derived. We propose a model for the automatic sediment type classification of the side-scan sonar data, which is based on convolutional neural networks (CNN). A big advantage of CNN is that they provide an end-to-end training: the CNN derives appropriate features automatically during the training process, which are then used for classification. The approach is based on a patch-wise classification using ensemble voting. The approach is evaluated on real world side-scan sonar data, which have been labelled using four classes (fine, sand, coarse, and mixed sediment) by experts. While the prediction of sand achieves an accuracy of 83 percent, the accuracy for fine sediment is very poor (11 percent).
AB - Spatially high-resolution information on the seabed sediment is import for many applications in the fields of oceanic engineering, coastal engineering, habitat mapping, and others. The seabed sediment is typically described by information based on the grain-size distribution, which are derived from sediment samples collected from the seafloor. For covering large areas side-scan sonar systems are typically used, which measure the backscatter intensity. From this information the sediment types can be derived. We propose a model for the automatic sediment type classification of the side-scan sonar data, which is based on convolutional neural networks (CNN). A big advantage of CNN is that they provide an end-to-end training: the CNN derives appropriate features automatically during the training process, which are then used for classification. The approach is based on a patch-wise classification using ensemble voting. The approach is evaluated on real world side-scan sonar data, which have been labelled using four classes (fine, sand, coarse, and mixed sediment) by experts. While the prediction of sand achieves an accuracy of 83 percent, the accuracy for fine sediment is very poor (11 percent).
UR - http://www.scopus.com/inward/record.url?scp=85046101954&partnerID=8YFLogxK
U2 - 10.1109/ssci.2017.8285220
DO - 10.1109/ssci.2017.8285220
M3 - Conference contribution
SP - 1
EP - 8
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -