Week 8 Lecture: IMAGE CLASSIFICATION

Nov 1 Lab 8: On-screen Digitization

Homework 6: Land Use/Land Cover Classification Schemes

Week 9 Lecture: UNSUPERVISED CLASSIFICATION

Nov 8 Lab 9: Unsupervised Classification

Homework 7: Spectral Clustering

Reading: CH 8; Field Guide CH 6

Week 10 Lecture: SUPERVISED CLASSIFICATION

Nov 15 Lab 10: Supervised Classification

Homework 8: Supervised Classification Algorithms

Reading: CH 8; Field Guide CH 6

Week 12 Lecture: CLASSIFICATION REDUX

Nov 22 Lab 11: Accuracy Assessment

Homework 9: Accuracy Assessment

Reading: CH 8, Field Guide CH 6

Week 13 Lecture: INTERFACE OF REMOTE SENSING AND GIS

Nov 29 Lab 12: ERDAS GIS Analysis

Homework 10: NJ MapGarden

Reading: CH 10; Field Guide CH 10

Week 14 Lecture: CHANGE DETECTION

Dec 6 Lab 13: NJ Change Detection

Reading: CH 9

Week 15 Lecture: FUTURE DIRECTIONS

Dec 13 Lab 14: Internet Scavenger Hunt

Remote Sensing Applications article review due

Take-home exam distributed

Dec 17 Take-home exam due by 4:00 PM

16:450:615 Seminar in Remote Sensing Fall 1999

COURSEWORK EXPECTATIONS:

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.

There will be one take-home exam for both Part I and II. These exams will be test on the material covered in lecture, lab and the reading. There will be a final lab project (Lab Project II) on multispectral image classification. 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. Lab assignments are hands-on exercises using the ERDAS image processing work stations. During lab periods, students will work in groups (of 2 to 3) 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.

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. No eating or drinking is allowed in the lab.

GRADING:

Part I

Take-home Exam 100 points

Homework 15 points

Labs 35 points

Article Review/critique 25 points

Part II

Take-home Exam 100 points

Homework 15 points

Labs 35 points

Article Review/critique 25 points

Final Project 150 points

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Total 500 points