[gdal-dev] Raster statistics

[Chris Waigl]

I would not use gdal for this particular task. I presume you have the band data in a 2D numpy array. Then I’d get the 80th percentile for example with np.percentile() and use a boolean expression to generate a mask for the array (droneraster > perc80value ).

Chris

-- 
Christine (Chris) Waigl - cwaigl at alaska.edu -  +1-907-474-5483 - Skype: cwaigl_work
Geophysical Institute, UAF, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA







> On Aug 3, 2017, at 5:43 AM, Paul Meems <bontepaarden at gmail.com> wrote:
> 
> I have a drone raster file which I want to use for some calculation.
> Before the calculation, I need to loose some extreme values.
> I want to do something like a percentile calculation where you get all values, order them and loose the top 10%.
> For this, I need to get all values first which can be slow when using a large file.
> 
> I looked at the statistics (band.GetStatistics) but that doesn't work well.
> I thought I could use 2 times the standard deviation added to the mean to get roughly 97%.
> But with these statistics:
>     STATISTICS_MAXIMUM=33.186080932617
>     STATISTICS_MEAN=24.840205979603
>     STATISTICS_MINIMUM=1.5951598882675
>     STATISTICS_STDDEV=4.7285348016053
> Mean + 2*std is larger than the max.
> 
> So I moved to the histogram. It is also very fast, but I'm not sure how to use it.
> I have this:
>   256 buckets from 1.53322 to 33.248:
>   410 77 66 66 65 58 56 45 42 87 57 72 61 65 68 70 73 82 93 ...
> Does this mean, bucket 1 = 410 that I have 410 pixels of value 1.53322 and the second bucket means I have 77 pixels between 1.53322 and 1.657? 1.657 = 1.53322 + ((33.248 - 1.53322)/256)
> 
> Is this a good approach? Or can/should I use a different one.
> 
> Thanks,
> 
> Paul
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