Commit bbe2e8c3 authored by Daniel Scheffler's avatar Daniel Scheffler
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parent 618546a8
Pipeline #4344 passed with stages
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......@@ -50,7 +50,8 @@ multi-sensoral/multi-temporal images. It supports a wide range of input data for
line (without any Python experience) or as a normal Python package.
**Global co-registration - fast but only for static X/Y-shifts:**
Global co-registration - fast but only for static X/Y-shifts
Only a global X/Y translation is computed within a small subset of the input images (window position is adjustable).
This allows very fast co-registration but only corrects for translational (global) X/Y shifts.
......@@ -61,9 +62,11 @@ also allows to align the output image to the reference image coordinate grid if
Here is an example of a Landsat-8 / Sentinel-2 image pair before and after co-registration using AROSICS:
.. image:: docs/images/animation_testcase1_zoom_L8_S2_global_coreg_before_after_1066x540.gif
:width: 500
**Local co-registration - for spatially variable shifts but a bit slower**:
Local co-registration - for spatially variable shifts but a bit slower
A dense grid of tie points is automatically computed, whereas tie points are subsequently validated using a
multistage workflow. Only those tie points not marked as false-positives are used to compute the parameters of an
......@@ -74,6 +77,7 @@ Here is an example of the computed shift vectors after filtering false-positives
(mainly due to clouds in the target image):
.. image:: docs/images/shift_vectors_testcase1.png
:width: 500
For further details check out the `documentation <>`!
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