Module 5 Supervised and Unsupervised classification of Raster data
In this module I learned some really useful skills in classifying Raster data from aerial imagery photos.
The ability to automate the processes of classifying pixels by the color reflectance is extremely helpful when working with large areas that have many types of land cover and land use.
In this lab we classified 8 different categories of LULC which you can see in the legend.
I compared the mean histograms of each signature and decided that bands 4,5,6 had the most seperation which led me to believe that they possessed the least confusion of all the bands.
Once I brought the raster image in to Arc GIS pro the layout creation was very straight forward due to the help I had from the Good examples provided by Brian Fulfrost. The good examples of layouts gave me a clear objective and goal of how I wanted the data to be represented so that the viewer was not distracted from the main point I was trying to convey.
The inset map fit neatly in the top left hand corner and the Land area for each classified attribute is calculated in Acreage.
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