GIS5027 Module 4: Spatial Enhancement, Multispectral Data, and Band Indices

     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 from lab (such as if a spike is on the left side of a histogram, the feature we are looking for will be dark). We utilized different band combinations to determine what would be the best combination to highlight the feature we want to show to our audience. Each map below indicates a subset image I thought would best represent the answers to questions this lab laid out to be sought after and showcased: 


Q1: “In Layer_4 there is a spike between pixel values of 12 and 18. Name the type of feature responsible for this and locate an example of it on the map.”

Q2: “Identify the feature that represents both A) a small spike in layers 1-4 around pixel value 200, and B) a large spike between pixel values 9 and 11 in Layer_5 and Layer_6.”


Q3: “In certain areas of water, layers 1-3 to become much brighter than normal, layer 4 becomes somewhat brighter, and layers 5-6 to remain unchanged.”





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