Sentinel-1-based flood mapping: a fully automated processing chain

In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

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Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2013.2272953 ◽

2013 ◽

Vol 10 (6) ◽

pp. 1617-1621 ◽

Cited By ~ 154

Author(s):

T. Esch ◽

M. Marconcini ◽

A. Felbier ◽

A. Roth ◽

W. Heldens ◽

...

Keyword(s):

Processing Chain ◽

Automated Processing ◽

Global Data ◽

Urban Footprint

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Design and Implementation of Data Usability Processor into an Automated Processing Chain for Optical Remote Sensing Data

Communications in Computer and Information Science - Leveraging Applications of Formal Methods, Verification, and Validation ◽

10.1007/978-3-319-51641-7_2 ◽

2016 ◽

pp. 21-37

Author(s):

Erik Borg ◽

Bernd Fichtelmann ◽

Christian Fischer ◽

Hartmut Asche

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Optical Remote Sensing ◽

Sensing Data ◽

Design And Implementation ◽

Processing Chain ◽

Automated Processing ◽

Data Usability

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Data Usability Processor for Optical Remote Sensing Imagery: Design and Implementation into an Automated Processing Chain

Lecture Notes in Computer Science - Computational Science and Its Applications – ICCSA 2013 ◽

10.1007/978-3-642-39643-4_46 ◽

2013 ◽

pp. 635-651

Author(s):

Erik Borg ◽

Bernd Fichtelmann ◽

Hartmut Asche

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing ◽

Remote Sensing Imagery ◽

Design And Implementation ◽

Processing Chain ◽

Automated Processing ◽

Data Usability

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Convergent-beam thickness determination: The advantages of digital imaging

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100169833 ◽

1994 ◽

Vol 52 ◽

pp. 420-421

Author(s):

Stuart McKernan ◽

C. Barry Carter

Keyword(s):

Intensity Variation ◽

Convergent Beam Electron Diffraction ◽

Digital Data ◽

Thickness Determination ◽

Frequent Use ◽

Automated Processing ◽

Beam Thickness ◽

Convergent Beam ◽

Remarkable Agreement

Convergent-beam electron diffraction (CBED) patterns contain an immense amount of information relating to the structure of the material from which they are obtained. The analysis of these patterns has progressed to the point that under appropriate, well specified conditions, the intensity variation within the CBED discs may be understood in a quantitative sense. Rossouw et al for example, have produced numerical simulations of zone-axis CBED patterns which show remarkable agreement with experimental patterns. Spence and co-workers have obtained the structure factor parameters for lowindex reflections using the intensity variation in 2-beam CBED patterns. Both of these examples involve the use of digital data. Perhaps the most frequent use for quantitative CBED analysis is the thickness determination described by Kelly et al. This analysis has been implemented in a variety of different ways; from real-time, in-situ analysis using the microscope controls, to measurements of photographic prints with a ruler, to automated processing of digitally acquired images. The potential advantages of this latter process will be presented.

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