Spectral Classification

For this week's lab, we learned how to complete an unsupervised and supervised classification on an aerial image in ERDAS Imagine. First, we practiced classifying an unsupervised image of UWF campus by accurately classifying different spatial and spectral resolutions. To finalize this, we reclassified and recoded images from the spectral signatures created in an AOI layer. 

After learning the steps needed to create a supervised classification map, I used the inquire tool to enter in the coordinates for all the features to begin creating spectral signatures (except for the roads and water-I created them without the inquire tool). When I completed creating the spectral signatures, I evaluated them using the Histogram and mean plots. The bands with the greatest distance from each other determined the color band that was used. For this map, I used R4, 5G, 3B. The output distance file did show some bright pixels that let me know the classification was not correct. I had to go back to my spectral signatures and adjust the Fallow, Water, and Road area. I could never get the Road and Urban area perfect but I finally got an image that made sense.