4.5.3. Clustering¶

This tab allows for the Clustering of a Band set. In particular, K-means and ISODATA methods are available.

A report .txt is saved along with the classification, containing the class spectral signature and the spectral distance thereof.

4.5.3.1. Clustering of band set ¶

Select input band set : select the input Band set;
Method K-means ISODATA: select the clustering method K-means or ISODATA;
Distance threshold : if checked, for K-means: iteration is terminated if distance is lower than threshold; for ISODATA: signatures are merged if distance is greater than threshold;
Number of classes : number of desired output classes;
Max number of iterations : maximum number of iterations if Distance threshold is not reached;
ISODATA max standard deviation : maximum standard deviation considered for splitting a class, for ISODATA algorithm only;
ISODATA minimum class size in pixels : desired minimum class size in pixels, for ISODATA algorithm only;
Use value as NoData : if checked, set the value of NoData pixels, ignored during the calculation;

Seed signatures from band values Use Signature list as seed signatures Use random seed signatures: select one options for seed signatures that start the iteration; the option Seed signatures from band values divides the spectral space of the Band set to get spectral signatures; the option Use Signature list as seed signatures uses the spectral signatures checked in ROI & Signature list; the option Use random seed signatures randomly selects the spectral signatures of pixels in the Band set;
Distance algorithm Minimum Distance Spectral Angle Mapping: select Minimum Distance or * Spectral Angle Mapping for spectral distance calculation;
Save resulting signatures to Signature list: if checked, save the resulting spectral signatures in the ROI & Signature list;
BATCH : add this function to the Batch;
RUN : choose the output destination and start the calculation;