Supervised and Unsupervised Classification

 Introduction to Unsupervised and Supervised Classification in ERDAS

In this week's lab we learned how to review and edit unsupervised and supervised classifications of aerial imagery. Through ERDAS Imagine you can set limits to the number of classes that each pixel could represent the categories. You can also reclassify the categories into to better summarize the image. 

Additionally, you can combine classifications by recoding the attributes and by doing so assigns a value that new categories are classified as. In our lab, after classifying each attribute to 5 classes we calculated the areas of each category. Since our data was using land classifications of forest, water, and agriculture areas we calculated the total area of permeable ( where water can be absorb into the soil such as vegetation, gravel, or turf) and impermeable ( where water is unable to be easily absorbs into the soil such as buildings, sidewalks, parking lots etc.). 

Supervised Classifications has a attribute table that already organized thematic raster data which takes less occupation space. Part of processing the data is to input the class type specifications into a process call Spectral Signatures that allows the Classifier to recognize the different features each data class has. We learned two forms of collecting spectral signatures through the Supervised Tab or through "Seed" a program that grows a region around specific geographic points of known land covers in the data set. 



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