Thursday, April 20, 2017
Thursday, April 6, 2017
Land Use Assessment of Pascagoula, MS
The objective of lab 10 was to assess and classify an aerial image of Pascagoula , MS. The image was classified according to features using polygons. Polygons were displayed using colors that best fit the features they symbolized. I had to go back and clip polygons from previous ones that I missed before finalizing the map. Overall, the LULC.shp file that I created made it easier to distinguish features within the image.
Monday, April 3, 2017
Thursday, March 23, 2017
The objective of Lab 8 was to perform an unsupervised classification in ArcMap and ERDAS, create values for pixels of select features, and combine pixels of the same feature into one class. Although the lab was tedious, it was interesting to note how tremendously shadows affect the way features are projected in an image. When categorizing the image into categories of trees, grass, building/roads, and shadows, I found that mixed values made it hard to assign values to a particular class. The image below is a classification at my discrepancy.
Tuesday, March 21, 2017
Thursday, February 23, 2017
In lab 6, students learned how to create multi-spectral images in ERDAS and ArcMap. A single image was created from multiple individual layers using Composite Bands in ArcMap and Layer Stack in ERDAS. I had to repeat the Import Data process in ERDAS Layer Stack because I did not set the Output File correctly. Multiple band combinations were used to distinguish features in composite images. I selected farmland as my map feature for the deliverable map. I chose to display the composite multi spectral image in NIR because it made it easy to differentiate between farmland and varies other vegetated lands. I also used a color scheme for a second composite image of the selected area.