A. Starling's GIS Blog

     Lab 5 in  Aerial Photo Interpretation and Remote Sensing   focused on unsupervised and supervised classification.       We used a provided a high resolution satellite image to determine unsupervised classification processes by first creating a cluster layer and comparing the cluster layer to the original high resolution image. We then reclassified the cluster layer by recoding within the Recode Table from 50 to 5 classes. Some pixels were shown to be a part of two classes relatively equally when we used the Swipe, Flicker, Blend, and Highlight tools. and we added a fifth class to account for Mixed (M) features that were very split between two classes. Unsupervised Classification      To utilize supervise classification, we used a new provided image to determine a new spectral signature with Signature Editor. Using both the “Drawing Polygon” method and “Creating Signature from Seed” method, we were able to look at the Spect...

     Lab 4 in Aerial Photo Interpretation and Remote Sensing gave us an opportunity to apply pre-processing steps to enhance images. We used Low Pass and High Pass filters to see the differences created when applied to an image. High Pass filter will allow the data (of pixels) that change significantly from pixel to pixel (“high frequency data”) to pass through the filter, while Low Pass suppresses those higher frequencies. We were able to visually see the difference between the two (as well as several other spatial filters) and conclude that Low Pass is good for large-scale pattern finding, but not as well suited to detail feature finding like High Pass filtering.     We explored and utilized histograms, the Inquire Cursor, and spectral band indices in ERDAS Image and how these tools/features help to identify and interpret image data. Ultimately, we looked at a region in Washington state and created subset images to show what we learned fro...

     This week’s lab focused on a brief EMR properties calculation session and giving us an introduction to ERDAS Imagine. We used Maxwell’s Wave Theory (C=lamda/nu) to see mathematically how the relationship between frequency and wavelength of electromagnetic radiation relate to each other (the smaller the wavelength, the higher the frequency). We used the Planck Relation (Q=h(nu)) to then show that the shorter the wavelength, the greater the energy per light photon in Joules.       Once done calculating, we dived into ERDAS Imagine. We learned about the basic tools, how to use the Viewer, and finally used the Inquire Box to help snap a portion of a provided thematic map with a focus on Olympic National Park, WA into a subset of the image. We added a new Area field to the attribute table within ERDAS Imagine, and once down, we added our subset into ArcGIS to be able to create a presentable map (see below). Thematic map of a subset (or portion) of Olym...