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Analysis of Land Cover Classes Using Unsupervised and Supervised Classification of Stennis Space ... - page 13 / 31

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Unsupervised Classification

First, an unsupervised classification was performed on the SSC Image using the ISODATA clustering method to classify the image into the desired classes (30) in the final output.

A thematic raster layer was generated using the ISODATA algorithm while running ERDAS IMAGINE

Thirty classes were derived in the classification with:

maximum number of iterations set to 12

convergence threshold set to 0.95

The pixels were identified for each of the categories and they were grouped into land cover categories:

water, shadow, coniferous trees,deciduous trees, mixed trees, scrub grass, grass, and urban

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