It's hard to believe, but this week's lab is the final lab of this awesome remote sensing class before I start my final project! With that being said, this week's lab was a great way to end the labs for the semester! This week we covered supervised and unsupervised classification. Unsupervised classification essentially gives the user the power to select how the image is classified using pixel values that match across spectral classes. These classes can then be changed individually so when you change the class, all pixel values in that class change. Supervised classification essentially uses the image analyst to supervise the selection and creation of spectral classes within the program. By defining the areas of classification, it then can classify the image based on a handful of 'seeded' areas.
For this week's deliverable, I was tasked with using supervised classification to classify the landcover/use of Germantown, Maryland. For this exercise, I created 8 seeded areas of interest classification signatures that would serve as the classification seeds for my image. I then chose a band combination (within the map title) that caused the least amount of spectral confusion and then recoded my values. Once my image was complete, I added the new class names and area of each class in acres. Additionally, I added and created a Distance map which shows where areas of classification are likely incorrect. The brighter the area, the higher the chance that the classification of that feature was wrong.
I am really glad that everyone has been following me through this incredible journey in the world of remote sensing and aerial photography, it is truly one of the coolest fields of GIS and I would love to work on this for my capstone research!
~Map On!
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