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/EES5053/ES4093, or permission of instructor.

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

Tuesday 3:00-5:00 or by appointment at room Sci. Bldg. 2.02.16

Lecture and Lab:

Tuesday: 5:30 – 8:50 pm, room: SB 2.01.02. 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 class without permission (prior to the class), you will loss 2 point (based on 100 points) per missing class.
• Actively participate (for those did not register the seminar class) graduate seminar recommended by the instructor, will be awarded 1 point per seminar no more than 5 points in total.
• Final grade will be 90-100 (A), 80-89 (B), 70-79 (C), 60-69 (D), and less than 60 (F).
• March 25 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 Tuesday and due right before the Tuesday 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 the class (you should let me know in advance). All lab exercises should use MS Word or others, please no handwriting (it is difficult to read). 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 26. 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 Feb12.  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: