Projects per year
Abstract
Seabed characterisation consists in the study of the physical and biological properties of the bottom of the oceans. It is effectively achieved with sonar, a remote sensing method that captures acoustic backscatter of the seabed. Classical Machine Learning (ML) and Deep Learning (DL) research have failed to successfully address the automatic mapping of the seabed from noisy sonar data. This work introduces the Deep Supervised Semantic Segmentation model for Seabed Characterisation (D4SC), a novel U-Net-like model tailored to such data and low-label regime, and proposes a new end-to-end processing pipeline for seabed semantic segmentation. That dual contribution achieves state-of-the-art results on a high resolution Synthetic Aperture Sonar (SAS) survey dataset.
Original language | English |
---|---|
Title of host publication | IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6932-6935 |
Number of pages | 4 |
ISBN (Electronic) | 9798350320107 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023 https://2023.ieeeigarss.org/ |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
---|---|
Volume | 2023-July |
Conference
Conference | 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 |
---|---|
Abbreviated title | IGARSS 2023 |
Country/Territory | United States |
City | Pasadena |
Period | 16/07/23 → 21/07/23 |
Internet address |
Keywords
- Deep Learning
- Seabed
- Semantic Segmentation
- Synthetic Aperture Sonar
Fingerprint
Dive into the research topics of 'D4SC: Deep Supervised Semantic Segmentation for Seabed Characterisation in Low-Label Regime'. Together they form a unique fingerprint.Projects
- 1 Active
-
DAP/21-11: Automatic, small-scale sea-floor characterization from high-resolution sonar data
Lopera Tellez, O. (Promotor), Neyt, X. (Promotor) & Arhant, Y. (Researcher)
1/09/21 → 31/08/25
Project: Research