Here is my Final Report for my Remote Sensing: Digital Image Processing and Analysis class. Using two Landsat 5 images from 1991 and 1999, I conducted a Change Detection using: Write Function Memory Insertion, Supervised Image Classification, and a Change Detection Matrix.
This change detection makes areas undergoing Urbanization far easier to visualize. Land Use Changes, with these methods can be quantified to see how much urbanization has undergone in a 9-year time frame.
Emphasizes large homogeneous areas
Edges and Boundaries are emphasized. Larger areas are deemphasized.
Meant to highlight points, lines, and edges in imagery
Also used to detect edges and enhances linear features oriented in a specific direction
Similar to Laplacian, used for edge detection.
Using the NDVI layer, a new Raster Color Slice was created to create a new form of Unsupervised Classification. The NDVI ranges I selected for each class were: Water (-0.202971 to -0.025000), High Density Urban (-0.025000 to -0.045000), Low Density Urban (0.045000 to 0.14500), Dark Vegetation (0.145500 to 0.300000), Very Green Vegetation (0.300000 to 0.621503).