Large-Scale MeanShift

Brief Description

Large-scale segmentation using MeanShift

Tags

Segmentation, LSMS

Long Description

This application chains together the 4 steps of the MeanShit framework, that is the MeanShiftSmoothing [1], the LSMSSegmentation [2], the LSMSSmallRegionsMerging [3] and the LSMSVectorization [4].

This application can be a preliminary step for an object-based analysis.

It generates a vector data file containing the regions extracted with the MeanShift algorithm. The spatial and range radius parameters allow adapting the sensitivity of the algorithm depending on the image dynamic and resolution. There is a step to remove small regions whose size (in pixels) is less than the given 'minsize' parameter. These regions are merged to a similar neighbor region. In the output vectors, there are additional fields to describe each region. In particular the mean and standard deviation (for each band) is computed for each region using the input image as support. If an optional 'imfield' image is given, it will be used as support image instead.

Parameters

Limitations

None

Authors

OTB-Team

See also

[1] MeanShiftSmoothing
[2] LSMSSegmentation
[3] LSMSSmallRegionsMerging
[4] LSMSVectorization

Example of use