Lab 5: Supervised Land Use Classification of Germantown, Maryland

This week’s lab focused on applying supervised image classification techniques using ERDAS Imagine to produce a current land-use map of Germantown, Maryland. The workflow required building a complete spectral signature set using AOIs at specific coordinates, refining signatures through histogram and mean plot evaluation, and running a maximum likelihood classification. I also created additional signatures for water and road surfaces to ensure the major land-cover types were accurately represented.

After generating the initial supervised classification, I recoded the output into eight final land-use classes—urban, road, grass, deciduous forest, mixed forest, fallow, agriculture, and water—and calculated the area for each category using the thematic attribute table. The final map includes the recoded classified image, an inset distance file showing areas of higher spectral uncertainty, and a legend with class names, colors, and area values.

Overall, this lab reinforced the importance of high-quality training signatures and careful class merging, and it demonstrated how supervised classification can support land-use monitoring and planning.