[GRASS-dev] [GRASS GIS] #3283: i.landsat.acca - cloud/shadow detection (vs. fmask)

[GRASS GIS]

#3283: i.landsat.acca - cloud/shadow detection (vs. fmask)
--------------------------+----------------------------
  Reporter:  martinl      |      Owner:  grass-dev@…
      Type:  enhancement  |     Status:  new
  Priority:  normal       |  Milestone:  7.4.0
 Component:  Imagery      |    Version:  unspecified
Resolution:               |   Keywords:  i.landsat.acca
       CPU:  Unspecified  |   Platform:  Unspecified
--------------------------+----------------------------
Description changed by martinl:

Old description:

> Recently I was working with Landsat data and performed cloud detection
> using G72:i.landsat.acca. It gave me quite reasonable results at least
> for
> clouds (see attachment:landsat_acca.png, yellow color). Much more worst
> results gave the module for shadows (orange color, see -s flag (1)), also
> `-2f` flags have been used. Please compare with RGB composition
> (attachment:landsat_rgb.png and attachment:landsat_acca_comp.png).
>
> The module uses basically map algebra for cloud detection (2) and for
> shadows (3). Shadows detection seems to be somehow unfinished (or very
> simple).
>
> I tested fmask (4) which gives much more better results
> (attachment:landsat_fmask.png, attachment:landsat_fmask_comp.png). I
> didn't studied the algorithm, but it seems to me that it's based on
> object segmentation approach.
>
> Do you think that new better implementation of G72:i.landsat.acca would
> be
> topic for GSOC or student project? The fmask algorithm seems to be
> well-known, I also found Python library (5).
>
> For now I would suggest to disable shadow detection or at least to add
> big fat warning about results. What do you think?
>
> (1) https://grass.osgeo.org/grass72/manuals/i.landsat.acca.html
>
> (2)
> https://trac.osgeo.org/grass/browser/grass/trunk/imagery/i.landsat.acca/algorithm.c#L76
>
> (3)
> https://trac.osgeo.org/grass/browser/grass/trunk/imagery/i.landsat.acca/algorithm.c#L465
>
> (4) https://github.com/prs021/fmask/blob/master/README.md
>
> (5) http://pythonfmask.org/en/latest/

New description:

 Recently I was working with Landsat data and performed cloud detection
 using G72:i.landsat.acca. It gave me quite reasonable results at least for
 clouds (see attachment:landsat_acca.png, yellow color). Much more worst
 results gave the module for shadows (orange color, see `-s` flag (1)),
 also `-2f` flags have been used. Please compare with RGB composition
 (attachment:landsat_rgb.png and attachment:landsat_acca_comp.png).

 The module uses basically map algebra for cloud detection (2) and for
 shadows (3). Shadows detection seems to be somehow unfinished (or very
 simple).

 I tested fmask (4) which gives much more better results
 (attachment:landsat_fmask.png, attachment:landsat_fmask_comp.png). I
 didn't studied the algorithm, but it seems to me that it's based on object
 segmentation approach.

 Do you think that new better implementation of G72:i.landsat.acca would be
 topic for GSOC or student project? The fmask algorithm seems to be
 well-known, I also found Python library (5).

 For now I would suggest to disable shadow detection or at least to add
 big fat warning about results. What do you think?

 (1) https://grass.osgeo.org/grass72/manuals/i.landsat.acca.html

 (2)
 https://trac.osgeo.org/grass/browser/grass/trunk/imagery/i.landsat.acca/algorithm.c#L76

 (3)
 https://trac.osgeo.org/grass/browser/grass/trunk/imagery/i.landsat.acca/algorithm.c#L465

 (4) https://github.com/prs021/fmask/blob/master/README.md

 (5) http://pythonfmask.org/en/latest/

--

--
Ticket URL: <https://trac.osgeo.org/grass/ticket/3283#comment:1>
GRASS GIS <https://grass.osgeo.org>