GIS4035/L Remote Sensing and Photo Interpretation, Instructor, Mr. Brian Fulfrost
Module 6 - Image Preprocessing and Spatial Enhancement
Image enhancements modify remote sensing data to help users to interpret those data beyond what is initially apparent. There are a variety of methods and purposes for enhancements, from extracting or highlighting hidden features, to correcting for sensor errors.
In this lab, students learned how to complete similar tasks in ArcMap and ERDAS Imagine. ArcMap and ERDAS cannot replace each other, but can be used in conjunction to complete the most difficult tasks.
Student Learning Outcomes:
- Download and import satellite imagery
- Perform spatial enhancements in ArcMap and ERDAS
- Utilize the Fourier Transformation function
STUDENT SPOTLIGHT AWARD
The BLOG postings this week illustrated that a number of students, including Alicia Lindbom and Brittany Burdelsky, and Sarah Love-Martin had a high level of understanding of how spatial image enhancement techniques can be be utilized for various functions, including the "destriping" of Landsat imagery. However, we thought Sara's BLOG posting was especially good.
Sarah is currently in her last semester with the GIS Online Program. She is a former Aerial Photographer (which sounds like the coolest job ever!) and has consistently shown herself to be a dedicated student. Congratulations and Welcome to the Spotlight!
Sarah's map and BLOG posting are easy to understand, provide an excellent overview of the spatial enhancement techniques learned in this week's Module, and demonstrate a high level of understanding of the material. Sarah provides a brief review of what spatial enhancement techniques are and then succinctly describes the differences and uses for each technique. In addition, she compare the similar tools for performing spatial enhancements in ArcGIS and Erdas Imagine. Her map clearly illustrates her success in applying the Fourier (and other) transformation methods to remove the "striping" on the Landsat image. The map also includes a "zoomed in" example of the original image so the reader can compare her results to the same area in the original, which allows the reader to easily understand the outcome of the methods that were applied.