AFAIK
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.".
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.
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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