Module 10 Lab: Supervised Classification

In module 10 we were tasked with adding unique values to all pixels in a raster image. Supervised classification involves collected sets of pixels to define spectral signatures. I then had to evaluate the accuracy and use them to classify the entire image. Once I got the signatures recoded into eight specific classes I was able to notice some spectral confusion with in the roads signature. The area was far too large for what was actually in the given image. 

Module 9 Lab: Unsupervised Classification

Module 9 lab focused on unsupervised classification in order to determine the land cover of the UWF Campus. We used both Arcmap and ERDAS to perform these classifications, which were manually reclassified and recoded in order to simplify the data. I enjoyed using the functions in ERDAS, specifically the Swipe and Manual Flicker options.