ISODATA is a iterative Self-Organizing Data Analysis Technique.
It is uses spectral distance between image pixels in feature space to classify pixels into a specified number of unique spectral groups.
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The ISODATA method uses minimum spectral distance to assign a cluster for each candidate pixel. The process begins with a specified number of arbitrary cluster means or the means of existing signatures, and then it processes repetitively, so that those means shift to the means of the clusters in the data.
To perform ISODATA clustering; NTM
- N – maximum number of clusters to be considered. Since each cluster is the basis for a class, this number becomes the maximum number of classes to be formed. The ISODATA process begins by determining N arbitrary cluster means. Some clusters with too few pixels can be eliminated, leaving less than N clusters.
- T – a convergence threshold, which is the maximum percentage of pixels whose class values are allowed to be unchanged between iterations.
- M – maximum number of iterations to be performed
- Number of clusters: 10 to 15 per desired land cover class.
- Convergence threshold: percentage of pixels whose class values should not change between iterations; generally set to 95%
- Maximum number of iterations: ideally, the convergence threshold should be reached. Should set “reasonable” parameters so that convergence is reached before iterations run out.
A ISODATA iterations, pixels assigned to clusters with closest spectral mean; mean recalculated; pixels reassigned.
Continues until maximum iterations or convergence threshold reached.