Fundamentals, algorithms, and techniques of remote sensing image processing, information extraction and analysis, including radiometric and geometric corrections, image enhancement, image sharpening, principal components analysis, image classification (pixel-based, object-oriented, and decision tree), spectral analysis, vectorization, integration with GIS, etc.. Beyond the techniques, the student should develop an understanding of what kind of information can be extracted from different types of imagery, what kind of application needs what types of imagery, and what the limitations of the imagery are.

 Prerequisites: ES5053, or permission of instructor.

Dr. Hongjie Xie , Email: hongjie.xie@utsa.edu, Tel: 210-458-5445
Department of Earth and Environmental Sciences at UTSA
http://www.utsa.edu/LRSG

Friday 2:00-4:00 or by appointment at room Sci. Bldg. 2.02.16

Lecture and Lab:

Friday: 5:30 – 8:20 pm, room: SB 2.03.04. Lecture in the first 1.5 - 2 hours, and lab in the rest of the time. You are required to attend all lectures and labs except you have a good excuse (you should let me know prior to the class).

Textbook:

Introductory Digital Image Processing: A Remote Sensing Perspective (3rd edition), John R. Jensen, 2005, Pearson Prentice Hall

Online materials:
NASA Remote Sensing Tutorial, http://rst.gsfc.nasa.gov/Front/tofc.html
CCRS Fundamentals of Remote Sensing, http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter1/chapter1_1_e.html
Remote Sensing & Image Analysis, P. Gong, http://nature.berkeley.edu/~gong/textbook/
USGS Earthshots, http://edcwww.cr.usgs.gov/earthshots/slow/tableofcontents

Grade Policy:

The final grade for the course will be determined as below:

Lab exercises    40%
Final exam        20%
Term project     40% 

Active class participation and exceptional performance in term project will be rewarded with extra credits.  However, if you miss a lecture/lab without permission (prior to a class/lab), you will loss 2 points (based on 100 points) per missing lecture/lab.

March 28 is the last day to drop an individual course or withdraw from all classes and receive an automatic grade of "W".

Lab exercise:

Lab exercise will be assigned on Friday and due right before the class in the following week. Late exercise is unacceptable, unless you do have an good excuse. No make-up lab exercise. Email submission is unacceptable unless you have to miss a class/lab. Lab exercise is very important for you to actually understand the image processing techniques,  to use image processing software package, and to solve real world problems.

Term project:  

A large portion (40%) of this class is a term project. You will submit a complete proposal (5%) (no less than 2 pages, double space, 12 font) with a title, student name, introduction or question statement, data and methods to be used, and expecting results on Feb 11. You will be asked to report the progress (10%) on March 25 (no less than 6 pages, double space, 12 font). All students will give a 15 minutes class presentation (10%) and a final project paper (15%) (no less than 10 pages, single space, 12 font). Instructor will also give some topics for your reference. More details will be given in the Term Project Assignment on Jan 28.  You are always very welcome to discuss with me about your project.

Academic dishonesty policy:

All work must be original. Plagiarizing or cheating in any form will be reported and a failing mark will be assigned.

Tentative Schedule: