| Title: |
On the detection and segmentation of metocean features on SAR images using Deep Learning: perspectives for SWOT |
| Authors: |
Longépé, Nicolas; Husson, Romain; Wang, Chen; Mouche, Alexis; Tandeo, Pierre |
| Contributors: |
Collecte Localisation Satellites (CLS); Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER); Lab-STICC_IMTA_CID_TOMS; Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC); École Nationale d'Ingénieurs de Brest (ENIB); Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Bretagne Sud (UBS)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB); Institut Mines-Télécom Paris (IMT); Département Signal et Communications (IMT Atlantique - SC); IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) |
| Source: |
SWOT Science Team Meeting ; https://imt-atlantique.hal.science/hal-02156712 ; SWOT Science Team Meeting, Jun 2019, Talence, France |
| Publisher Information: |
CCSD |
| Publication Year: |
2019 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
[STAT.AP]Statistics [stat]/Applications [stat.AP]; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere |
| Subject Geographic: |
Talence; France |
| Time: |
Talence, France |
| Description: |
International audience ; The above classification model (one 20x20 km imagette = one label) is then used over a full Wide Swath S-1 image using local class estimates with a convolution approach to provide a semantic segmentation. Multi-class (and multi-label , i.e. one pixel several classes) semantic segmentation is now feasible with DL , pending starting with a good and sufficient training database, and active learning process to enrich it, To this end, annotation tools and adequate framework should be consolidated and tuned to our problematic: 1) massive processing needed to raise specific issues (for instance, "sea ice" North of Madagascar should be re-tagged as internal waves), 2) additional information from ancillary metocean data might be provided , … The entire S-1 Wave Mode archive from 2016 is being processed. Below the occurrence of each class as classified by the DL model is provided on a monthly basis. This classified database could be used as input for a systematic collocation process with SWOT data. That will 1) help to understand Ka-band near nadir imaging processes for a given phenomenon, 2) serve during the Cal/val campaign, and 3) be used to build a training database with tagged SWOT images serving also DL model. With the upcoming launch of SWOT, new Ka-band near-nadir SAR image will be produced. Whereas the legacy of ocean SAR imaging is huge for L-C-or X-band SAR sensors with intermediate incidence angle, the interaction of Ka-band near-nadir EM waves and its associated SAR image formation lead to some uncertainties on how metocean features will be imaged on SWOT image. To name a few, atmospheric fronts, ocean fronts, rain cells, convective microcells, internal waves, gravity waves, biological slicks, upwelling or wind trails are phenomena that will be imaged by SWOT. These phenomena could be a source of errors and bias for SSH products. Meanwhile, they are of potential interest for the scientific communities. In this study, we aim to propose a methodology to flag and detect these phenomena ... |
| Document Type: |
conference object; still image |
| Language: |
English |
| Availability: |
https://imt-atlantique.hal.science/hal-02156712; https://imt-atlantique.hal.science/hal-02156712v1/document; https://imt-atlantique.hal.science/hal-02156712v1/file/20190619_SWOTPoster_DLOcean.pdf |
| Rights: |
https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.C15C07FB |
| Database: |
BASE |