Lab 9 Unsupervised classification using ERDAS - UWF high resolution .sid image using only visible bands of light (RGB)

This lab provided an exercise in both ArcGIS and ERDAS Imagine to perform an unsupervised classification.  The deliverable component came from the ERDAS Exercise 2 portion of the lab.  We utilized the Unsupervised tool within the Raster tab Classification group and set the nuber of classes to 50, accepted the approximate True Color for the color scheme and assigned Red as 3, Green as 2 and Blue as 1; we also used a maximum iteration of 25 and a convergence threshold of 0.950 and set the skip factors to 2 for X and Y.  Our next task was to reclassify the 50 classifications we just created via the attribute table.  We selected known items in the image which were the highlighted in the table; we then set the Class_Name field to one of our 5 categories: trees, buildings/road, grass, shadow and mixed; we also changed the color of our new class to something appropriate.  We repeated this process until we were left with items which were difficult to discern and placed them in the mixed category.  We then turned on the original .sid image and used a combination of the Swipe, Flicker, Blend and Highlight tools by first selecting an unclassified item in the attribute table, changing it's color to something distinct and bright and using the tools to aid in identifying the item at which point we could properly reclass it.  This process was repeated until all 50 classes had been reclassified and assigned the required color.  At this point we used the Raster Tab, Thematic button Recode to merge our 50 classes into 5 for our final product.  This new recoded  image was then saved.  The image was imported into my ArcMap geodatabase and a final map created.  We also added a new column for Area to the attribute table.  These were then summed and used to develop permeable and impermeable acreages and subsequently percentages.  This information was included in the final map.