IsoData classification. Remote Sensing

IsoData Clustering (also known as Iteractive Self-Organizing Data Analysis Technique) is an unsupervised machine learning technique used to classify remote sensing data. The algorithm works by iteratively partitioning the data into a specified number of clusters based on the statistical properties of the data.

An illustration of IsoData Clustering in remote sensing can be visualized as follows:

First, a set of pixels in a remote sensing image are randomly selected as initial cluster centers.

Next, all other pixels in the image are assigned to the cluster whose center is closest to it.

The cluster centers are then re-calculated as the mean of the pixels assigned to each cluster.

Steps 2 and 3 are repeated until the cluster centers no longer move significantly.

The final result is a set of clusters with each cluster representing a unique land cover type or feature in the image.

It is a simple and easy to implement algorithm, which can be useful in remote sensing applications where the number of clusters is unknown and needs to be determined from the data.

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