LSMSSmallRegionsMerging - Exact Large-Scale Mean-Shift segmentation, step 3 (optional)

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

Detailed description

Given a segmentation result (can be the out output parameter of the LSMSSegmentation application [2]) and the original image, it will merge segments whose size in pixels is lower than minsize parameter with the adjacent segments with the adjacent segment with closest radiometry and acceptable size.

Small segments will be processed by increasing size: first all segments for which area is equal to 1 pixel will be merged with adjacent segments, then all segments of area equal to 2 pixels will be processed, until segments of area minsize. For large images one can use the tilesizex and tilesizey parameters for tile-wise processing, with the guarantees of identical results.

The output of this application can be passed to the LSMSVectorization application [3] 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 LSMSSmallRegionsMerging .

[1]Table: Parameters table for Exact Large-Scale Mean-Shift segmentation, step 3 (optional).
Parameter Key Parameter Name Parameter Type
in Input image Input image
inseg Segmented image Input image
out Output Image Output image
minsize Minimum Segment Size Int
tilesizex Size of tiles in pixel (X-axis) Int
tilesizey Size of tiles in pixel (Y-axis) Int
ram Available RAM (Mb) Int
inxml Load otb application from xml file XML input parameters file
outxml Save otb application to xml file XML output parameters file
  • Input image: The input image, containing initial spectral signatures corresponding to the segmented image (inseg).
  • Segmented image: Segmented image where each pixel value is the unique integer label of the segment it belongs to.
  • Output Image: The output image. The output image is the segmented image where the minimal segments have been merged. An ecoding of uint32 is advised.
  • Minimum Segment Size: Minimum Segment Size. If, after the segmentation, a segment is of size lower than this criterion, the segment is merged with the segment that has the closest sepctral signature.
  • 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.
  • Available RAM (Mb): Available memory for processing (in MB).
  • 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_LSMSSmallRegionsMerging -in smooth.tif -inseg segmentation.tif -out merged.tif -minsize 20 -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 LSMSSmallRegionsMerging application
LSMSSmallRegionsMerging = otbApplication.Registry.CreateApplication("LSMSSmallRegionsMerging")

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

LSMSSmallRegionsMerging.SetParameterString("inseg", "segmentation.tif")

LSMSSmallRegionsMerging.SetParameterString("out", "merged.tif")

LSMSSmallRegionsMerging.SetParameterInt("minsize", 20)

LSMSSmallRegionsMerging.SetParameterInt("tilesizex", 256)

LSMSSmallRegionsMerging.SetParameterInt("tilesizey", 256)

# The following line execute the application


This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) 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] LSMSegmentation
[3] LSMSVectorization