Remote Sensing - GEOG 470

Meeting: TTH 10:30-11:45 AM in PSB 310
Instructor: Matt McGranaghan
Office: PSB 313 Phone: 956-7092 Email:
Office Hours: I'm generally around but make an appointment to be sure.

Description: The course is an introduction to digital remote sensing and digital image processing. It will cover collection, processing and interpretation of remotely sensed data. We will study digital thermal, microwave, and multispectral image data characteristics and collection systems. Concepts and techniques for processing and interpreting these data will be the focus of lectures and homework/lab exercises.

Readings: The text will be John A. Richards and Xiuping Jia Remote Sensing Digital Image Analysis: An Introduction which is now in its third edition. In addition, a few chapters of Lillesand and Kiefer's Remote Sensing and Image Interpretation pertaining to image interpretation will be placed on reserve for your reference.

Software: This term we will be using primarily ENVI 4.3. You may find ERDAS imagine, Photoshop, GIMP, IDRISI, ArcGIS, GRASS, and other assorted tools and untilities of use as well.

Grading and Requirements: Grades will be based on the combined scores on the following components:

The exams will be mixed short answer and essay format and will draw upon readings, lectures, and exercises. The homework/lab exercises will involve doing image processing and will generally require a brief write-up. The rest of the components are intended to allow you to explore parts of remote sensing which are of special interest or utility to you. The short paper is 3-5 pages identifying five themes of interest to you in the remote sensing literature. The term paper proposal should be 1-2 pages outlining what you will do for the term paper/project. The term paper/project should be about equivalent to a 15 page paper, but the balance of paper and project will influence this.

All of the components must be completed to finish the course. I will sum the component scores, plot them, and look for natural breaks in the distribution. Final grades will depend on individual performance and on the performance of the class as a whole. Our objective is learning. Read, think, and experiment with the ideas, images and software. Interact with others in the class to clarify the material. The University policy on incompletes will be followed.