Hello Jordi
I have a problem using the imagetimeseries gapfilling application. i have 
concatenated the 20m resolution bands and have also prepared the masks for 
the different bands but now do not know exactly which mask to use, or do i 
also need to concatenate the masks following the same sequence of bands and 
dates like the input time series image (concatenated)?

Le vendredi 8 juin 2018 15:33:22 UTC+1, O Buck a écrit :
>
> Hy Jordi,
>
> thanks Jordi. The problem is, that my time stack is not very dense 
> (Landsat from 1990). I have a maximum of 10 images per path/row, if I allow 
> max cloud cover of 80%.
> Have you had experience with Landsat-5 data and the TimeSeriesGapFilling 
> tool?
> My focus classes are agriculture, so I can not interpolate the data too 
> far.
> Regards
> Oliver
>
>
>
> Jordi Inglada <[email protected] <javascript:>> schrieb am Fr., 8. Juni 
> 2018 16:25:
>
>>
>> Hi, 
>>
>> If you have time series, you can use the ImageTimeSeriesGapFilling 
>> application which will interpolate the time profile if you provide the 
>> corresponding cloud masks.
>>
>> Jordi
>>
>> On Fri 08-Jun-2018 at 15:42:31 +0200, "'O Buck' via otb-users" <
>> [email protected] <javascript:>> wrote: 
>> > Thanks Cedric for your reply. 
>> >
>> > Yeap I was thinking of this. Problem is that I have pretty cloudy 
>> images (old Landsat 5 data from 1990), which means I have "no-data" cloud 
>> pixels in all bands at different locations. If I mask out all pixels that 
>> have a no-data value in at least one band, my layerstack image will hardly
>> > contain any "valid" pixels.
>> > I was hoping that the RF would ignore no-data values and train with 
>> whatever pixel dimension it has. Lets say I have a multi-temporal 
>> layerstack image conposed of five aquisition images with 6 bands each (i.e. 
>> 30 bands in total), the pixel information available would range from 0 
>> (always
>> > cloudy) to 30 bands (never clouds).
>> >
>> > But maybe I am completely wrong, due to my lack of deeper RF knowledge.
>> >
>> > Oliver
>> >
>> > Am Freitag, 8. Juni 2018 15:22:21 UTC+2 schrieb Cédric Traizet:
>> >
>> >  Hi Oliver,
>> >
>> >  A no-data value is treated like any other value by the classifier and 
>> used for the classification.
>> >
>> >  If you don't want to process pixels with no-data value(s), you could 
>> build a no-data mask from your input image and the ManageNoData application 
>> in buildmask mode (
>> https://www.orfeo-toolbox.org/CookBook/Applications/app_ManageNoData.html) 
>> and use it as mask parameter
>> >  of the ImageClassifier application (masked pixels will have the class 
>> label 0).
>> >
>> >  Sincerely,
>> >
>> >  Cédric
>> >
>> >  Le 08/06/2018 à 13:11, 'O Buck' via otb-users a écrit :
>> >
>> >  Dear all, 
>> >
>> >  does anybody know, how the implemented RF (Random Forest) classifier 
>> treats no data values. I have a layerstack image consisting of n-Bands. 
>> Bands 1-6 represent one image acqisition date t1 (blue, green, red....), 
>> Bands 7-12 represent the image date t2...,
>> >
>> >  Within each band there are no-data values (set to -1) caused by a 
>> preprocessing step masking clouds, shadows, image artefacts ....
>> >
>> >  If I put this layerstack into the RF classifier, there classification 
>> results is produced, yet I am not sure about the no-data areas. How are 
>> they treated by the RF. Is it tolerable to include the no-data values. Are 
>> there better classifiers implemented?
>> >
>> >  Regards
>> >  Oliver
>> >  -- 
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