4.9.3 Segmentation

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Detailed description

This application allows performing various segmentation algorithms on an multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output.

In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images.

To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option allows applying to try to stitch together polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

This section describes in details the parameters available for this application. Table 4.50, page 516 presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is Segmentation.

Input Image The input image to segment

Segmentation algorithm Choice of segmentation algorithm (mean-shift by default) Available choices are:

• Mean-Shift: OTB implementation of the Mean-Shift algorithm (multi-threaded).
  • Spatial radius: Spatial radius of the neighborhood.
  • Range radius: Range radius defining the radius (expressed in radiometry unit) in the multispectral space.
  • Mode convergence threshold: Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations.
  • Maximum number of iterations: Algorithm iterative scheme will stop if convergence hasn’t been reached after the maximum number of iterations.
  • Minimum region size: Minimum size of a region (in pixel unit) in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. If set to 0 no pruning is done.
• Edison mean-shift: Edison implementation of mean-shift algorithm, by its authors.
  • Spatial radius: Spatial radius defining neighborhood.
  • Range radius: Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space.
  • Minimum region size: Minimum size of a region in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry.
  • Scale factor: Scaling of the image before processing. This is useful for images with narrow decimal ranges (like [0,1] for instance).
• Connected components: Simple pixel-based connected-components algorithm with a user-defined connection condition.
  • Condition: User defined connection condition, written as a mathematical expression. Available variables are p(i)b(i), intensity_p(i) and distance (example of expression : distance <10 )
• Watershed: The traditional watershed algorithm. The height function is the gradient magnitude of the amplitude (square root of the sum of squared bands).
  • Depth Threshold: Depth threshold Units in percentage of the maximum depth in the image.
  • Flood Level: flood level for generating the merge tree from the initial segmentation (between 0 and 1)

Processing mode Choice of processing mode, either raster or large-scale. Available choices are:

• Tile-based large-scale segmentation with vector output: In this mode, the application will output a vector file or database, and process the input image piecewise. This allows performing segmentation of very large images.
  • Output vector file: The output vector file or database (name can be anything understood by OGR)
  • Writing mode for the output vector file: This allows setting the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format.
  • Mask Image: Only pixels whose mask value is strictly positive will be segmented.
  • 8-neighbor connectivity: Activate 8-Neighborhood connectivity (default is 4).
  • Stitch polygons: Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel.
  • Minimum object size: Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization.
  • Simplify polygons: Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database.
  • Layer name: Name of the layer in the vector file or database (default is Layer).
  • Geometry index field name: Name of the field holding the geometry index in the output vector file or database.
  • Tiles size: User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null.
  • Starting geometry index: Starting value of the geometry index field
  • OGR options for layer creation: A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation.
• Standard segmentation with labeled raster output: In this mode, the application will output a standard labeled raster. This mode can not handle large data.
  • Output labeled image: The output labeled image.

Examples

Example 1 Example of use with vector mode and watershed segmentationTo run this example in command-line, use the following:

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

Example 2 Example of use with raster mode and mean-shift segmentationTo run this example in command-line, use the following:

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

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. MeanShift filter results depends on threads number.

Authors

This application has been written by OTB-Team.

See also

These additional ressources can be useful for further information:

• MeanShiftSegmentation