id,summary,reporter,owner,description,type,status,priority,milestone,component,version,resolution,keywords,cc,cpu,platform 3283,i.landsat.acca - cloud/shadow detection (vs. fmask),martinl,grass-dev@…,"Recently I was working with Landsat data and performed cloud detection using G72:i.landsat.acca. The module produced quite reasonable results at least for clouds (see attachment:landsat_acca.png, yellow color), but much more worst results 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/",enhancement,new,normal,7.8.3,Imagery,unspecified,,i.landsat.acca,,Unspecified,Unspecified