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On the detection and segmentation of metocean features on SAR images using Deep Learning: perspectives for SWOT

Abstract : 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 to 1) mask them when generating L2 ocean products (SSH, wind…) and thus avoid any additional errors, 2) improve our understanding of Ka-band near nadir ocean SAR imaging while 3) being of interest for scientific community. This study is based on Sentinel-1 C-band SAR images for which the 3 above objectives are still relevant. Within the frame of Copernicus S-1 mission, high-resolution SAR images are produced each day, representing a daily average of 3.45 TB of published data.
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Contributor : Pierre Tandeo Connect in order to contact the contributor
Submitted on : Friday, June 14, 2019 - 4:04:00 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM


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  • HAL Id : hal-02156712, version 1


Nicolas Longépé, Romain Husson, Chen Wang, Alexis Mouche, Pierre Tandeo. On the detection and segmentation of metocean features on SAR images using Deep Learning: perspectives for SWOT. SWOT Science Team Meeting, Jun 2019, Talence, France. ⟨hal-02156712⟩



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