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. 

Final map

Module 4 Lab: Spatial Enhancement, Multispectral Data, and Band Indices

 

Image with darker pixel values

Image with higher pixel values

Image with different levels of reflectivity

In this week's lab, we were asked to perform exercises with included tasks to increase our understanding of spatial enhancement, multispectral data, and band indices. These exercises and tasks were to be conducted in ERDAS Imagine and ArcGIS Pro. In the first part of the lab, we analyzed images using different methods of filtering for high pass, low pass, and sharpen filter. The high pass filters offer advantages when looking at edges using the Range statistic creating edge detect. The low pass filter generalizes the images because the filter is being run on images that have already been filtered. Lastly, the sharpen filter is similar to the high pass filter only slightly sharpens details. In the last part of the lab, we examined histograms to locate three areas in an image based on pixel values. Grouping of pixel values towards one end over another was how we were able to determine these locations. One where the pixel value was too low, showing darker colors, another showing lighter colors, and the final one showed different levels of reflectivity of one color. 

Module 3a and 3b Lab: Intro to ERDAS Imagine

This week’s lab introduced us to ERDAS Imagine, software that is raster-based and provides tools to extract information from images while being able to change the bandwidth of the images to study them more in-depth. It was a fun lab and the biggest challenges were making sure to go “by the numbers” in lab exercise steps and working with formulas calculating the frequency, wavelength, and energy of photons for the process. We added an image provided to us and manipulated it to get an output that would be added to ArcGIS. Once we added a random selection from the image to ArcGIS we calculated the hectares of an area and created a layout of map. 

Image from ERDAS loaded into ArcGIS

The intention of the lab was to familiarize ourselves with ERDAS, after all this is Remote Sensing. The software is somewhat similar to ArcGIS in the sense that there are tools, content panes, ways of adding data, and creating data as an output to be exported. One of the differences was how ERDAS handled the raster images allowing different bandwidths to be changed in the display. ERDAS was easy to work with as long as you are patient and have a fast computer when working on the server.