11:372:474       Undergraduate Advanced Remote Sensing                                Spring   2007

16:450:615/16:215:604    Graduate Seminar in Remote Sensing

                                   

Class Meeting: Mon 5:35-6:55 PM  ENR 123 

                       Wed 5:35-6:55 PM ENR 247 (CRSSA Teaching Lab)

 

Instructor:  Zewei Miao (Rick Lathrop)                                     E-mail: zmiao@crssa.rutgers.edu

http://www.crssa.rutgers.edu/courses/remsens                           Phone: 732 932-1583/2

                                                                                                Fax: 732-932-8746

 

Course Objectives: students should learn the fundamentals of digital analysis, interpretation and application of satellite remotely sensed imagery.  Students should develop an understanding of digital image processing techniques (including the basic data structures and algorithms involved) and become proficient in the hands-on application of these techniques using the ERDAS image processing workstations.  Students should learn not just how but also why and when to apply digital image processing techniques in the analysis of remotely sensed imagery.

 

Textbooks:  J. Jensen, Introductory Digital Image Processing, 3rd ed, Prentice-Hall, 2005;

ERDAS IMAGINE Field Guide (7th edition) (These two textbooks have been reserved for you at Chang Library, Cook Campus).

Graduate students: additional journal articles on reserve at Chang

                                   

Week 1    Lecture: INTRODUCTION TO SATELLITE IMAGE ANALYSIS

Jan 17                     Web Lecture 1 & Supplemental: Image Data Acquisition

                Homework 1: Ordering LANDSAT Images

Reading: Ch 1, 2, 3;  ERDAS CH. 1, 3

Remote Sensing Applications article review handed out

 

Week 2    Lecture: IMAGE DISPLAY AND ENHANCEMENT

Jan 22, 24               Web Lecture 2 & Supplemental: Image Statistics

                                Lab 1: Image Segmentation

                Homework 2: Image Statistics

                Reading: CH 4, 5:151-164, 8:255-272; ERDAS Ch. 4, 6:141-157, ERDAS App A Math Topics

 

Week 3    Lecture: IMAGE RESTORATION & ATMOSPHERIC CORRECTION

Jan 29, 31               Web Lecture 3

                                Lab 2: Image Normalization

                Homework 3: Landsat TM Thermal IR Calibration                        

                Reading: CH 6; ERDAS Ch. 5:132-135;

 

Week 4    Lecture: IMAGE RECTIFICATION

Feb 5, 7                  Web Lecture 4 & Supplemental: Cartography and Map Projections

                                Lab 3: Geometric Correction

                Homework 4: Geometric Correction

                Reading: CH 7; ERDAS CH 10, 13, App. B

 

Week 5    Lecture: SPATIAL ENHANCEMENT/FILTERING

Feb 12, 14              Web Lecture 5

                                Lab 4: Spatial Enhancement

                Homework 5: Spatial Filtering

                                Reading: CH 8:276-329; ERDAS Ch. 6:157-160, 189-201

                Remote Sensing Applications article review due

 

Week 6   Lecture: MULTI-IMAGE MANIPULATION 

Feb 19, 21              Web Lecture 6

                                Lab5: Principal Components Analysis

                                Homework 6: Principal Components Analysis                               

                Reading: CH 5:164-169, 8:274-276, 296-301; CH 11:443-445; Field Guide CH 6:162-183

                Take-home Exam Distributed. Due Wednesday Mar 8 in class.

 

Week 7   Lecture: VEGETATION INDICES

Feb 26, 28              Web Lecture 11

                                Lab 6: Vegetation Indices

                                Homework 6

                Reading: CH 8:301-322, CH 11:431-443, 457-462

 

Week 8  Lecture: HYPERSPECTRAL REMOTE SENSING                           

Mar 5, 7                  Web Lecture 12

                                Lab 7: Hyperspectral Remote Sensing

                Reading:  Field Guide CH 10-11

               

Week 9   Spring Break

Mar 12, 14

 

Week 10    Lecture: IMAGE CLASSIFICATION: UNSUPERVISED CLASSIFICATION

Mar 19, 21              Web Lecture 7

                                Homework 7: Spectral Clustering

                Reading: CH 9:379-389; Field Guide CH 7:221-225, 231-235

 

Week 11   Lecture: SUPERVISED CLASSIFICATION

Mar  26, 28             Web Lecture 8                                                                           

                                Lab 8: Supervised Classification

                                Homework 8: Supervised Classification Algorithms

                                Reading: CH 9:337-389; Field Guide CH  7:257-231, 235-253

 

                               

Week 12  Lecture: CLASSIFICATION REDUX: ADVANCED METHODS

Apr 2, 4                  Web Lecture 9   

                                Lab 9: Knowledge-based Classification

                                Reading: CH 9:389-401, CH 10, CH 11:445-457

                Return/Review take-home exam

 

Week 13  Lecture: ACCURACY ASSESSMENT

Apr 9, 11                Web Lecture 10

                                Lab 10: Accuracy Assessment

                Homework 9: Accuracy Assessment

                                Research paper/proposal due

                                Reading: CH 13, Field Guide CH 6

 

Week 14  Lecture: CHANGE DETECTION

Apr 16, 18              Lab 11: NJ Change Detection

                 Reading: CH 12

 

Week 15  Lecture: FUTURE DIRECTIONS

Apr 23, 25              Lab 12: Classification Project Due. Project  Synthesis.

 

Week 16

Class Project Presentations

Apr 30                    Graduate Project Presentations   

                                Take-home final exam distributed

 

May 7                     Final Take Home Exam Due

 

 

 

 

COURSEWORK EXPECTATIONS:                                      Remote Sensing            Spring 2007    

 

Reading assignments are expected to be read prior to the class date that is listed in the syllabus above.  Students are expected and encouraged to ask questions concerning the reading assignments and lecture material.  If you don't ask, I won't know you don't understand.

 

Homework assignments have been designed to supplement the lecture material and give the student added preparation in some of the details.  Homework will be distributed on Mondays and will be returned (completed) to Professor Lathrop the following Monday.   Each homework assignment is generally worth 3 points: 0 - not completed; 1 - unsatisfactory; 2 - satisfactory; 3 - excellent. Late homework will be downgraded by 1 point.

 

Lab assignments are hands-on exercises using the ERDAS image processing work stations.  During lab periods, students will work in groups (of 2) to complete the exercises.  Interaction between students and the professor is expected and encouraged.  Students are encouraged to work in the CRSSA teaching lab, alone or with other class members, outside of normal class periods.  Don't let your lab partner do everything - students are expected to develop the proficiency to work unassisted on the ERDAS systems. There will be six lab assignments (5 pts each) during the first half of the semester.  Graduate students will have a major cumulative lab assignment during the second half (worth 50 points).

 

There will be a take-home exam and a final exam.  These exams will be on the material covered in lecture, lab and the reading. There will be a literature research paper due during the first half of the semester focussing on RS applications. 

 

There will be a final project incorporating hands-on image classification and/or change detection and/or RS/GIS integration, etc..  The work to complete the project will be done outside of normal class meeting times.  Each student is expected to work independently.  You can confer with other students on different approaches, techniques used, etc., but the final results and project writeup should be your own.  A separate handout concerning the project will be distributed later in the semester.

 

The CRSSA teaching lab is open 5 days a week (Monday to Friday) from 8:30AM to 6PM.  Additional weeknight and weekend hours will be posted. You will only be able to work on the ERDAS Image Processing systems during CRSSA's normal posted hours (check www.crssa.rutgers.edu/help/lab_sched_html).  No eating or drinking is allowed in the lab.

 

GRADING:     

Take-home Exam                                                                  100 points

                                Homework                                                                               30 points

Labs                                                                                         30 points           

Article Review/critique                                                         40 points

Final Exam                                                                             100 points

Final Project                                                                          150 points(ugrad)         200 (50 pts for classification, 150 for 2nd one) (grad)

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                                   Total                                                            450 points (ugrad)  500 pts (grad)