Tuesday, November 14, 2017

GIS 4035: Module 10 - Supervised Classification


Above is a map of Germantown, MD's Land Use. In this lab we learned how to further classify images in ERDAS by creating AOI layers that allow us to create our own signatures. By using the polygon and grow properties tool, we were able to pinpoint certain coordinates and areas and classify them in the image. After re-coding the signatures, we were left with 8 unique classifications that are displayed on the map. 

Tuesday, November 7, 2017

Module 9 - Unsupervised Classification


This week we learned how to perform an unsupervised classification in both ArcMap and ERDAS. We we were able to use tools and techniques such as iso cluster, maximum likelihood classification tool, swiping,  blending, flickering, and recoding. To demonstrate our new skills, we used an image of the UWF campus to classify 5 different area types.  The image above shows the breakdown of the 5 new categories and the percentage of acreage they cover in the land.