Max Class
Specifies the number of clusters (classes) desired.
Max Iteration
Specifies the maximum number of iterations in calculating the
cluster mean positions.
Min Threshold
Specifies the movement threshold as a fraction of the cluster
means. If the movement of all cluster means is less than the minimum
threshold, then the algorithm has converged. The default is 1%.
Max Sample Size
Specifies the number of
samples to collect on which to perform the
the iterative clustering. This defaults to 262144 if not provided
by the user. If the indicated number is larger than the total
number of pixels in the image, then all the pixels in the image
will be used.
The time to compute each iteration is proportional to the number of samples used. This means that using a lot more than the default number of samples can make the clustering process much slower. Also, all the samples are stored in memory, meaning that a large NSAM value can lead to higher memory requirements. With 262144 samples and five bands of eight bit input data, the program would require approximately 1.3MB of memory while with NSAM set to 2000000, it would take 10MB of memory.
Background
Any pixel with this value will be ignored during the
classification and will be assigned to class 0 ( NULL class).