From July 1st, 2020, and for a period of two years, I have been appointed as Distinguished Lecturer of the IEEE-GRSS Geoscience and Remote Sensing Society.
The Distinguished Lecturer Program is a service of the GRSS and its members to
support the chapter activities. The goal is to provide chapters with access to with
leading professionals in geoscience and remote sensing and discuss novel topics in
current research. This is an opportunity for the GRSS membership to hear interesting
talks about work being done in in geoscience and remote sensing.
In particular, I will take car of the following topics:
- Basics of SAR Polarimetry,
- SAR Polarimetry: Theory and Applications,
- SAR, SAR Polarimetry & Multitemporal #SAR Statistical Description
➲ More information
R. Pelich, M. Chini, R. Hostache, P. Matgen and C. López-Martínez, “Coastline Detection Based on Sentinel-1 Time Series for Ship- and Flood-Monitoring Applications,” in IEEE Geoscience and Remote Sensing Letters, Early Access, 2020
➲ Full paper
This letter addresses the use of the Sentinel-1 time series with the aim of proposing an automatic and unsupervised coastline detection method that averages the dynamical variations of coastal areas over a limited period of time, e.g., one year. First, we propose applying a temporal averaging filter that allows the temporal variations in coastal areas, e.g., due to tides or vegetation, to be encapsulated, and, at the same time, the speckle to be reduced, without decreasing the spatial resolution of the synthetic aperture radar (SAR) time series. Then, based on the distinctive backscattering values of the sea and land pixels, we will employ an iterative hierarchical tiling method in order to accurately characterize the two classes using bimodal distribution. The distribution is then segmented by a thresholding and region-growing procedure to separate the sea and land classes. A large-scale quantitative comparison between the SAR-derived and open street map (OSM) coastlines allows for a numerical evaluation of the results, i.e., an overall agreement ranging from 80% to 90%. In addition, Sentinel-2 images are used to evaluate the estimated SAR coastline qualitatively. Furthermore, the benefits of having an accurate SAR coastline are shown in the case of two well-known Earth observation-monitoring applications, ship detection, and floodwater mapping.