[Qgis-developer] min/max for rasters

Radim Blazek radim.blazek at gmail.com
Mon Dec 16 01:58:39 PST 2013

On Sat, Dec 14, 2013 at 7:57 AM, aperi2007 <aperi2007 at gmail.com> wrote:
> The strategy of 2-98 is usually useful when there a "noised image", because
> we assume that the noise is a white noise and it
> is randomized an isolated spikes.
> THis is absolutely a right theory and really useful,
> ma what kind of imagge are usually used in a gis system.
> If we think at the ortophoto image the noise could be really happened
> because they came from a photo-sensor.
> But is we think to a artificial image, like the 2-colors balck-white images
> named "carta tecnica" thata are trasposition of vectorial data.
> Them are no noised images and has a really thin lines. Also the artifical
> thematic chars with colors and point and symbols and lines (outline and so
> on) are noise-less images.
> Don't forget to think also to geological charts. Are all noise-less images.
> So what kind of image are more used in a GIS system ?
> This is not simple question.
> The response is , "it is dependent by the kind of work you should do.".

Yes, so we can either ask user when QGIS is run the first time to set
some global defaults (nobody will do (correctly)) or we can try to
guess the type of data which will never be 100% but it can be better
than it is now.

> But also another question is:
> Usually the ortophoto are not simple to have . They are produced and have a
> license.
> The thematic images are more easy to produce and are often without a license
> or has a free license.
> More often the ortophoto images are available from a WMS system, and this is
> a solution that deny the use of the 2-98 strategy.

BTW, we can reconsider to decompose WMS images to RGB bands.


> Andrea.
> On 14/12/2013 07:17, Paolo Cavallini wrote:
>> Il 13/12/2013 20:18, Radim Blazek ha scritto:
>>>> Can you describe some examples where 2-98% is a problem (data type,
>>>> number of bands, map content, features/phenomena represented by those
>>>> 2+2%,...) so that we can think about it better?
>> Example #1 (less problematic): dtm and their legend are always shown
>> wrong; newbies do not understand why
>> Example #2 (more serious): rasterizing sparse vectors (e.g. rivers)
>> results in a black rectangle, as the number of pixels with valid data is
>> <2%.
>> In fact, I think we should help users more, e.g. by applying non linear
>> colour scaling (log, exp)  in case of very skewed raster values
>> distribution: if data are more or less normally distributed, no cut is
>> applied, and linear scaling is used; if they are badly skewdw or with
>> outliers, apply a non linear colour scaling. With some thinking, this
>> should solve most if not all user cases, without asking a normal user to
>> understand much about raster stats.
>> However, in my case the general setting "use min/max" does not seem to
>> be working.
>> Thanks for your thoughts.
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