publication . Conference object . Article . 2019

A Deep Neural Network Slope Reduction Model on Sentinel-1 Images for Water Mask Extraction

Mpakratsas, Marios; Moumtzidou, Anastasia; Gialampoukidis, Ilias; Vrochidis, Stefanos; Kompatsiaris, Ioannis;
Open Access English
  • Published: 15 Oct 2019
  • Publisher: Zenodo
Abstract
The monitoring of water bodies from space is one of the main challenges for flood risk assessment, food security and climate change monitoring applications. A common issue in Synthetic Aperture Radar (SAR) images water-bodies masks generation problem is falsely identifying as water, areas that are characterised by a complex morphology. Existing works on water bodies estimation either remove the steep slope areas or not, on a case-by-case manner. The deep learning era allows for automatic adaptation to Sentinel 1 images where the false removal of water, in cases of rivers and lake shores, can be avoided. This paper proposes a DNN model that generates water-bodies...
Subjects
free text keywords: water bodies, Synthetic Aperture Radar, surface water mapping, Deep Learning, water bodies, Synthetic Aperture Radar, surface water mapping, Deep Learning
Funded by
EC| EOPEN
Project
EOPEN
EOPEN: opEn interOperable Platform for unified access and analysis of Earth observatioN data
  • Funder: European Commission (EC)
  • Project Code: 776019
  • Funding stream: H2020 | RIA
Validated by funder
Download fromView all 5 versions
Open Access
https://dx.doi.org/10.5281/zen...
Conference object . 2019
Provider: Datacite
Open Access
ZENODO
Conference object . 2019
Provider: ZENODO
Open Access
https://dx.doi.org/10.5281/zen...
Conference object . 2019
Provider: Datacite
Any information missing or wrong?Report an Issue