Kelsey Yoder-Ostroski's Portfolio of Geographic Information Science

For this week in GIS4035, we learned about unsupervised and supervised classification using ERDAS Imagine.  The included map is my supervised classification product of Germantown, Maryland. I "seeded" sample sites by using either neighborhood or Euclidean spectral distance, which were then turned into spectral signatures. After collecting over a dozen feature sites, I analyzed the spectral bands of different features by comparing their histograms and mean plots. This information was used to determine the best bands for identifying and separating each feature class. The spectral signatures were recoded to combine similar classes and each class was re-colored for readability.  An output distance image file was created to identify if any areas may have been misclassified. As seen above, there are some brighter pixels in what may be agricultural or fallow fields. I chose not to create any new training sites here as I had no way to ground truth the image.

This week in GIS4035 we were introduced to spatial enhancement techniques, multispectral data, and band indices. First, we were tasked with downloading data through GLOVIS, which is surprisingly nice for a government website. After acquiring the data, we needed to preprocess it. In ERDAS Imagine we used a 3x3 low pass filter, a 3x3 high pass filter, and a 3x3 sharpen filter. In ArcGIS Pro, we experimented with other filters, such as focal stats mean and range. We also compared histogram manipulation techniques in both programs. Lastly, we experimented with multispectral bands to highlight or suppress features in the image.

  This week in GIS4035, we were introduced to ERDAS Imagine. It seems to have a lot in common with ArcGIS Pro and I am sure I will learn a great deal about it in the coming weeks. The map we created depicts the different land cover classifications of a small area of raster imagery. My son is sitting on my lap as I write this. He asked why is it all blocks and, in a way, I think that question encapsulates this week's lesson. I explained to him, each block (pixel) represents a block just like Minecraft. Each block can only be one thing: dirt, rock, grass, etc. The more blocks you have, the more you can build. The higher the radiometric value, the more we can build!