Commit 214283b4 authored by Guillaume Pasero's avatar Guillaume Pasero

DOC: add a description of the all-in-one LSMS application

parent e22e6521
......@@ -124,7 +124,6 @@ segmentation of very large image with theoretical guarantees of getting
identical results to those without tiling.
It has been developed by David Youssefi and Julien Michel during David
internship at CNES.
For more a complete description of the LSMS method, please refer to the
......@@ -262,6 +261,35 @@ set the tile size using the *tilesizex* and *tilesizey* parameters.
However unlike the *LSMSSegmentation* application, it does not require
to write any temporary file to disk.
The *LargeScaleMeanShift* application is a composite application that chains
all the previous steps:
- Mean-Shift Smoothing
- Segmentation
- Small region merging
- Vectorization
Most of the settings from the previous applications are also exposed in this
composite application. The range and spatial radius used for the segmentation
step are half the values used for Mean-Shift smooting, which are obtained from
LargeScaleMeanShift parameters. There are two output modes: vector (default)
and raster. When the raster output is chosen, last step (vectorization) is
otbcli_LargeScaleMeanShift -in input_image.tif
-spatialr 5
-ranger 30
-minsize 10
-mode.vector.out segmentation_merged.shp
There is a cleanup option that can be disabled in order to check intermediate
outputs of this composite application.
Dempster Shafer based Classifier Fusion
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