Unsupervised Image Classification
In this week’s lab, students learned to perform unsupervised classification using both ArcGIS and ERDAS Imagine. Along with running the classification process, students were asked to determine what feature the new image classes represent (reclassification) and then simplify many classes into a few types of features (recoding). The map submitted was a reclassified and recoded image of the UWF property including text identifying impermeable and permeable surfaces as percentages.
Student Learning Outcomes:
- Perform an unsupervised classification in both ArcMap and ERDAS
- Accurately classify images of different spatial and spectral resolutions
- Manually reclassify and recode images to simplify the data
STUDENT SPOTLIGHT AWARDS
The following student was chosen for their exception work on the Unsupervised Image Classification assignment:
What we like: In this week’s lab, Justin’s reclassification and recoding was exceptional.The combination of both Justin's (a) description of the process and technique of unsupervised classification, and his (b) map, which was well designed and clear, demonstrated his high level of comprehension of the week's material on automated image classification methods. Justin also provided a brief critique of unsupervised classifications including (1) the limitations of unsupervised classification of spectral values to distinguish every pixel as pervious or impervious cover classes, and (2) issues with the pixel values of the imagery (e,.g. shadows). Although the vast majority of students did very well on this week's lab, Justin's map and description provide the best overall blog posting.