JCLambert GIS Enterprise: Module 5: Unsupervised & Supervised Classification

In the first exercise, the UWF campus was used for our data. We used a MrSID file (multiresolution seamless image database), specifically a high-resolution aerial photograph of the UWF campus. In ERDAS for this part, I completed an unsupervised classification of the UWF image. I loaded the image with the correct parameters. Once loaded, under the Raster tab, the details mattered for the setup in the analysis. Name the Input and Output rasters, Output Signature unchecked, Number of Classes to 50. I had to change the color schemes to R3, G2, and B1. Setting the skip factors to 2 helped expedite the processing time because of how every other pixel gets analyzed. Once the analysis is complete, I reclassified the 50 classes in the attribute table. The reclassification meant changing the colors and establishing categories for the colors to represent. After the reclass and setting the classifications, I recoded the Class Names. This is an extension, my words, of the reclass process. It is necessary to establish a connection with the data for the final product (file). In Recode the values entered are to combine the reclass values into one numbered value, i.e., 1-4 for Grass then becomes 1 for all four of the Grass features in the image. Once these are Merged it is easier to conduct analysis, I calculated the percentage difference between permeable and impermeable surfaces.

In the second exercise, we used Grays Harbor, Washington imagery. In this part, I conducted a Signature Collection for Supervised Classification. Once the image is loaded and the Signature Editor tool is opened, I need the Drawing tool to draw polygons around areas for coordinates that are given. This is one way of gathering the data I wanted for an Area of Interest (AOI). The next way is by Creating Signatures from the AOI Seed tool by growing a region around an area where land cover is known. Two signatures that are used with this tool are Spectral Euclidean Distance and Neighborhood. When using this tool, is very similar to the previous method but this one is from the Inquire Legacy box where I set the At Inquire and the distance value, 11 for the Spectral Euclidean Distance, but it could be anywhere from 0-255 for pixel value. I captured the areas of interest as polygons, saved them and now they are ready for analysis.