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From "Martin Desruisseaux (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SIS-446) NetCDF store should be robust to non-linear localization grid
Date Fri, 08 Mar 2019 18:28:00 GMT

     [ https://issues.apache.org/jira/browse/SIS-446?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Martin Desruisseaux updated SIS-446:
------------------------------------
    Issue Type: Improvement  (was: Task)

> NetCDF store should be robust to non-linear localization grid
> -------------------------------------------------------------
>
>                 Key: SIS-446
>                 URL: https://issues.apache.org/jira/browse/SIS-446
>             Project: Spatial Information Systems
>          Issue Type: Improvement
>          Components: Referencing, Storage
>            Reporter: Martin Desruisseaux
>            Assignee: Martin Desruisseaux
>            Priority: Major
>             Fix For: 1.0
>
>
> Localization grid in netCDF files contain (longitude, latitude) values of all pixels.
{{LocalizationGridBuilder}} first computes a linear (affine) approximation of a localization
grid, then stores only the residual in its internal grid. The intent is to have small residual,
which make easier to compute the _inverse_ transformation (given a latitude and longitude,
find indices of the pixel at that location). However if the localization grid is non-linear,
then the affine transform is a poor approximation of that grid and the residuals are high.
High residual make _inverse_ transforms hard to compute, which result sometime in {{TransformException}}
being throw with a "no convergence" error message.
> In practice, the localization grid in netCDF files is often used for storing the results
of a map projection. The Mercator projection for example is often used. The proposed fix is
to allow {{LocalizationGridBuilder}} to try to transform the grid using a given list of map
projections and see if one of those projections results in a grid closer to a linear transform.
In other words, we should be able to "guess" what the projection may be. It is okay if the
guess is not a perfect match; if the residuals become smalls, it will resolve the "no convergence"
errors.



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