LSMSSegmentation - Exact Large-Scale Mean-Shift segmentation, step 2

This application performs the second step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS) [1].

Detailed description

This application will produce a labeled image where neighbor pixels whose range distance is below range radius (and optionally spatial distance below spatial radius) will be grouped together into the same cluster. For large images one can use the tilesizex and tilesizey parameters for tile-wise processing, with the guarantees of identical results.

Filtered range image and spatial image should be created with the MeanShiftSmoothing application outputs (fout and foutpos) [2], with modesearch parameter disabled. If spatial image is not set, the application will only process the range image and spatial radius parameter will not be taken into account.

Please note that this application will generate a lot of temporary files (as many as the number of tiles), and will therefore require twice the size of the final result in term of disk space. The cleanup option (activated by default) allows removing all temporary file as soon as they are not needed anymore (if cleanup is activated, tmpdir set and tmpdir does not exists before running the application, it will be removed as well during cleanup). The tmpdir option allows defining a directory where to write the temporary files.

Please also note that the output image type should be set to uint32 to ensure that there are enough labels available.

The output of this application can be passed to the LSMSSmallRegionMerging [3] or LSMSVectorization [4] applications to complete the LSMS workflow.


This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is LSMSSegmentation .

[1]Table: Parameters table for Exact Large-Scale Mean-Shift segmentation, step 2.
Parameter Key Parameter Name Parameter Type
in Filtered image Input image
inpos Filtered position image Input image
out Output labeled Image Output image
spatialr Spatial radius Float
ranger Range radius Float
minsize Minimum Segment Size Int
tilesizex Size of tiles in pixel (X-axis) Int
tilesizey Size of tiles in pixel (Y-axis) Int
tmpdir Directory where to write temporary files Directory
cleanup Temporary files cleaning Boolean
inxml Load otb application from xml file XML input parameters file
outxml Save otb application to xml file XML output parameters file
  • Filtered image: The filtered image, corresponding to the fout output parameter of the MeanShiftSmoothing application.
  • Filtered position image: The filtered position image, corresponding to the foutpos output parameter of the MeanShiftSmoothing application.
  • Output labeled Image: This output contains the segmented image, where each pixel value is the unique integer label of the segment it belongs to. It is recommended to set the pixel type to uint32.
  • Spatial radius: Threshold on Spatial distance to consider pixels in the same segment. A good value is half the spatial radius used in the MeanShiftSmoothing application (spatialr parameter).
  • Range radius: Threshold on spectral signature euclidean distance (expressed in radiometry unit) to consider pixels in the same segment. A good value is half the range radius used in the MeanShiftSmoothing application (ranger parameter).
  • Minimum Segment Size: Minimum Segment Size. If, after the segmentation, a segment is of size lower than this criterion, the segment is discarded.
  • Size of tiles in pixel (X-axis): Size of tiles along the X-axis for tile-wise processing.
  • Size of tiles in pixel (Y-axis): Size of tiles along the Y-axis for tile-wise processing.
  • Directory where to write temporary files: This applications need to write temporary files for each tile. This parameter allows choosing the path where to write those files. If disabled, the current path will be used.
  • Temporary files cleaning: If activated, the application will try to remove all temporary files it created.
  • Load otb application from xml file: Load otb application from xml file.
  • Save otb application to xml file: Save otb application to xml file.


To run this example in command-line, use the following:

otbcli_LSMSSegmentation -in smooth.tif -inpos position.tif -out segmentation.tif -spatialr 5 -ranger 15 -minsize 0 -tilesizex 256 -tilesizey 256

To run this example from Python, use the following code snippet:


# Import the otb applications package
import otbApplication

# The following line creates an instance of the LSMSSegmentation application
LSMSSegmentation = otbApplication.Registry.CreateApplication("LSMSSegmentation")

# The following lines set all the application parameters:
LSMSSegmentation.SetParameterString("in", "smooth.tif")

LSMSSegmentation.SetParameterString("inpos", "position.tif")

LSMSSegmentation.SetParameterString("out", "segmentation.tif")

LSMSSegmentation.SetParameterFloat("spatialr", 5)

LSMSSegmentation.SetParameterFloat("ranger", 15)

LSMSSegmentation.SetParameterInt("minsize", 0)

LSMSSegmentation.SetParameterInt("tilesizex", 256)

LSMSSegmentation.SetParameterInt("tilesizey", 256)

# The following line execute the application


This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) [1] and may not be suited for any other purpose. This application is not compatible with in-memory connection since it does its own internal streaming.


This application has been written by David Youssefi.

See Also

These additional resources can be useful for further information:
[1] Michel, J., Youssefi, D., & Grizonnet, M. (2015). Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 53(2), 952-964.
[2] MeanShiftSmoothing
[3] LSMSSmallRegionsMerging
[4] LSMSVectorization