Unsupervised classification

Unsupervised classification is a method used in remote sensing and image analysis to categorize pixels or objects within an image into different classes or clusters without prior knowledge of their characteristics. Unlike supervised classification, which requires training data with known class labels, unsupervised classification relies solely on the inherent structure and statistical properties of the data to identify natural groupings or patterns. Here are some key points about unsupervised classification:

1. Clustering Algorithms: Unsupervised classification algorithms aim to partition the data into clusters based on similarities in spectral or spatial characteristics. Common clustering algorithms include K-means clustering, hierarchical clustering, and self-organizing maps (SOMs). These algorithms iteratively group pixels or objects into clusters, minimizing intra-cluster variability and maximizing _____ __________ _______ _______ _________ ____ _________ _________ _____ _________ ________.
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