Michigan Technological University School of Forest Resources and Environmental Science Digital Image Processing: A Remote Sensing Perspective FW5560 Spring Semester 2009 Instructor: Ann Maclean 189 Forest Resources and Environmental Science 487-2030 [email protected] Office Hours: Tuesday 11- 12 and Friday 10 - 12 or by appointment Text: Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd edition John Jensen, 2005 Pearson Education Inc, Pearson Prentice Hall References: Computer Processing of Remotely Sensed Images, An Introduction, 3rd edition Paul M. Mather, 2004 Wiley Publishing Multipsectral Image Analysis Using the Object-Oriented Paradigm, 1st edition Kumar Navulur, 2006 CRC Press Assessing the Accuracy of Remotely Sensed Data: principles and Practices Russell G. Congalton and Kass Green, 1999 CRC Press Class Website: http://forest.mtu.edu/classes/fw5560/ Introduction: As the ease of access to remote sensed imagery increases, it is important to understand how to derive information from the data, assess the accuracy of that information and have the skills to interpret the information. Digital image processing has evolved rapidly as a sophisticated process to extract quantitative, biophysical data and land use/cover information and utilize that information as input to spatially distributed models to help us understand various ecosystems and their processes. Class Objectives: This class is designed to teach you the basics of digital image processing through a combination of lectures and lab exercises using the ERDAS Imagine image processing software and ArcMap GIS software.Expectations: It is important that you attend all lectures and labs and be on time for both. Each lecture and lab will build on knowledge and skills presented in previous sessions. If you fall behind it is very difficult to catch up due to the extensive technical vocabulary and the software skills needed to perform the analyses in the lab exercises. Basic math and statistical skills are a prerequisite for this class. You need to be able to do algebraic calculations as easily as you can add and subtract, as well as calculate a mean, standard deviation, variance and correlation and be able to interpret the output. We will also be using multivariate statistics as well. Syllabus Week 1: January 12 - 16 Lectures: What is remote sensing, what is digital image processing? Types of resolution, image formats Hardware and software considerations Lab: Introduction to ERDAS Imagine and downloading imagery Readings: Chapters 1, 2 and 3 (Chapter 2 is for review) Week 2: January 19 - 23 Lectures: Image quality Evaluation of image quality, metadata, basic statistics (a review) Geostatistical analysis Lab: No lab due to Martin Luther King Holiday Reading: Chapter 4 Week 3: January 26 - 30 Lectures: Initial display alternatives Scientific visualization, use of LUTs Preprocessing of remotely sensed data Lab: Assessment of image quality Reading: Chapter 5 Week 4: February 2 - 6 Winter Carnival Recess, February 5 and 6- no lecture Thursday Lecture: Radiometric corrections Lab: Radiometric corrections Readings: Chapters 6, handout Week 5: February 9 – 13 First Hour Exam, Thursday, February 12th Lectures: Geometric corrections Lab: Geometric correction Readings: Chapter 7Week 6: February 16 - 20 Lectures: Image enhancements Lab: Image enhancements Reading: Chapter 8 Week 7: February 23 - 27 Lectures: Image transformations Lab: Principal components and tassel cap transformations Reading: Chapter 8 Week 8: March 2 - 6 Lectures: Supervised classification algorithms- parallelepiped, minimum distance to mean, maximum likelihood- the foundation algorithms Lab: Training set selection and assessment Readings: Chapter 9 SPRING BREAK: March 9-13 Week 9: March 16 - 20 Lectures: Unsupervised classification- ISODATA, the unsupervised foundation algorithm Discussion of supervised classification outcomes Lab: Supervised classification Readings: Chapter 9 Week 10: March 23 - 27 Lectures: Expert system classification Lab: Unsupervised classification Reading: Chapter 9 Week 11: March 30 – April 3 Second Hour Exam, Tuesday March 31 Lectures: Expert systems, decision trees, Lab: Expert classification Reading: Chapter 10 Week 12: April 6 -10 Lectures: Thematic map accuracy assessment Lab: Accuracy assessment Readings: Chapter 13 and handouts Week 13: April 13-17 Lectures: Change detection: a survey of methodologies Lab: Change detection application Readings: Chapter 12 and handoutsWeek 14: April 13 - 17 End of Term exam- take home Presentations of lab work Grade Composition 2 Exams @ 20% each 40% Lab Reports 50% Lab Presentation 10% Grading Scale 93-100 A 90-92 AB 83-89 B 80-82 BC 70-79