[GRASS-dev] [EXTERNAL] GSoC 2018 final report week 13 - GRASS GIS module for Sentinel-2 cloud and shadow detection
doug_newcomb at fws.gov
Wed Aug 15 05:00:35 PDT 2018
Thank you for this work!
On Mon, Aug 13, 2018 at 4:21 AM Roberta Fagandini <robifagandini at gmail.com>
> Hi all!
> I'm Roberta Fagandini and this is the final report of my GSoC project.
> The title of the project is "GRASS GIS module for Sentinel-2 cloud and
> shadow detection". It adds new tools for the processing of Sentinel 2
> images to GRASS GIS software (Organization: OSGeo).
> Optical sensors are unable to penetrate clouds leading to related
> anomalous reflectance values. Unlike Landsat images, Sentinel 2 datasets do
> not include thermal and Quality Assessment bands that simplify the
> detection of clouds avoiding erroneous classification. At the same time,
> also clouds shadows on the ground lead to anomalous reflectance values
> which have to be taken into account during the image processing.
> The project creates a specific module for GRASS GIS application
> (i.sentinel.mask) which implements an automatic procedure for clouds and
> shadows detection for Sentinel 2 images. The procedure is based on an
> algorithm, developed within my PhD research, which allows to automatically
> identify clouds and their shadows applying some rules on reflectance values
> (values thresholds, comparisons between bands, etc.). These have been
> defined starting from rules found in literature and conveniently refined.
> In order to increase the accuracy of the final results, a control check is
> implemented. Clouds and shadows are spatially intersected in order to
> remove misclassified areas. The final outputs are two different vector maps
> (OGR standard formats), one for clouds and one for shadows.
> To run i.sentinel.mask, the bands of the desired Sentinel 2 images have to
> be imported and the atmospheric correction has to be applied.
> In order to make the data preparation easier, another GRASS GIS addon module
> has been developed within the GSoC project.
> i.sentinel.preproc is a module for the preprocessing of Sentinel 2 images
> (Level-1C Single Tile product) which wraps the import and the atmospheric
> correction using respectively two existing GRASS GIS modules,
> i.sentinel.import and i.atcorr.
> *The state of the art before the project:*
> Before this GSoC 2018 project, no modules for the detection of clouds and
> shadows were available for Sentinel 2 images. Only a specific module for
> Landsat automatic cloud coverage assessment was available within GRASS GIS
> (i.landsat.acca) while regarding shadows, no specific module was available.
> Moreover, performing the atmospheric correction was a bit complicated
> especially for unexperienced users who have to process one band at a time
> and provide all input parameters manually.
> *The added value that the project brought to GRASS GIS:*
> Now a specific module for clouds and shadows detection, i.sentinel.mask, is
> available in GRASS GIS.
> Moreover, i.sentinel.preproc provides a simplified module which allows
> importing images and performing the atmospheric correction avoiding users
> to supply all the required input parameters manually. The module should
> help users in preparing data to use as input for i.sentinel.mask. In fact,
> it makes especially the atmospheric correction procedure easier and faster
> because it allows performing atmospheric correction of all bands of a
> Sentinel 2 scene with a single process and it retrieves most of the
> required input parameters from the image itself. Moreover, one of the
> possible output of i.sentinel.preproc is a text file to be used as input
> for i.sentinel.mask.
> *Follow up:*
> Both i.sentinel.mask and i.sentinel.preproc are complete and working
> modules which can be easily installed with g.extension from the official
> GRASS GIS SVN repository.
> Obviously, they can be improved therefore the next steps could be:
> - Implementation of other existing algorithms of clouds and shadows
> detection (i.sentinel.mask)
> - Implementation of a new download procedure avoiding dependencies
> - Integration of the Topographic Correction (i.sentinel.preproc)
> NOTE: Implementation of other existing algorithms of clouds and shadows
> detection was one of the possible goals of the GSoC project but the coding
> and debugging of some parts of the two addons required more time than
> *Permanent links:*
> *Code developed during the GSoC coding period: *
> *Codes on the official GRASS GIS SVN repository:*
> *Weekly reports: *
> *Images to showcase the project:*
> Kind regards,
Doug Newcomb - Cartographer
551F Pylon Dr
919-856-4520 ext. 14 doug_newcomb at fws.gov
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