GEO-SCI426/626 Remote Sensing and Image Interpretation

Department of Geosciences

University of Massachusetts - Amherst

4 cr, Fall 2019

Class schedule table         Syllabus in PDF

Notes, lab and assignments will be updated on the Moodle.

Lecture††††††††††††††††† TuTh 10-11:15 am    †††††††††††† 129 Morrill Sci. Ctr.

Lab ††††††††††††††††††††††† Tu 2:30-4:30 pm††† (No lab in the first week), 212 Morrill IV South (UMass IT lab)

Instructor††††††††††††† Prof. Qian Yu,  Morrill 267,  qyu@geo.umass.edu, Office hour: Mon 10-11, Th 11:15-12:30or by appointment

TA††††††††††††††††††††††††† Monica Weisenbach, Morrill 264, mweisenbach@umass.edu,

Office hour: Tu 11:15-12:30 Wed 10-11 am or by appointment

†††††††††††††

Course description

This course introduces the fundamentals of remote sensing. Class lectures will focus on a range of concepts and techniques key to understanding how remote sensing data are acquired, displayed, restored, enhanced, and analyzed. Topics include remote sensing principles, aerial photography, image interpretation, major remote sensing systems, image display and enhancement, information extraction, accuracy assessment, and remote sensing in environmental research and applications. First half of the semester focuses on theory of remote sensing. In the second half, we have several hands-on computer labs to gain experience using the image processing software ITT ENVI. We will also explore a range of practical issues related to the application of remote sensing to solve real world problems. Overall, this class emphasizes remote sensing theory and knowledge.

 

Course objectives

To provide you with an introduction to the principles and practices of photo interpretation and digital remote sensing for use in environmental monitoring, measurements of structural parameters, and natural resource management.

This class will ensure students have knowledge on these aspects:

1.the properties and characteristics of aerial photographs.

2.remote sensing systems: a) how to define the type of remote sensing needed to fulfill the userís stated objectives, b) where existing remote sensing data which fulfills his/her objectives may be located, and c) how to obtain new aerial photography, if necessary.

3.digital image processing: a) basic concepts on non-photographic remote sensing, b) general principles of digital image processing for remote sensing applications, and c) future applications of remote sensing to natural resource management and related fields.

4.remote sensing information extraction: a) which characteristics of land cover types can be mapped/measured from remote sensing, b) different techniques available for mapping and measuring these land cover types, and c) how accurately these land cover characteristics can be mapped from remote sensing.

Prerequisites:  High school Algebra and Geometry

Required textbook for lectures 

1.    Jensen, John R., 2016, Introductory Digital Image Processing: A Remote Sensing Perspective, Pearson, 4th ed. ISBN 978-0-13-405816-0.

Reference book for lectures

2.    Jensen, John R., 2007, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall: Upper Saddle River, NJ. 2nd ed. ISBN 0-13-188950-8

3.    Lillesand, Thomas M., 2007, Remote Sensing and Image Interpretation, Wiley. ISBN-13: 978-0470052457, 6th edition

4.    Gong, Peng, Remote Sensing and Image Analysis http://www.cnr.berkeley.edu/~gong/textbook/

Grading and evaluation

Exams will cover key concepts from lecture, article and laboratory activities. All written assignments must be handed in on time.

Exercises and assignments

Mid-Term Exam

Final Project

Presentation†††††

Class participation& attendance

30% 

35%

20% 

7%

8%

626 students:      1. Present a journal paper about remote sensing application in your field;

2. Choose your own topic for final project.

Laboratory activities and assignments: We will work through some laboratory activities specified in additional documents to aid in understanding technical concepts taught in lectures. We will also explore some of the technical facets of ITT ENVI software, which will help manipulate images. 

Policy on attendance and due-dates for assignments:

ē         Attendance to both lecture and lab session is required in the normal circumstances and forms a portion of your grade. For absences due to health reasons, family illness, religious observance or other extenuating circumstances, students should inform the instructor in a timely fashion. Verification document is requested for alternate arrangements to be made. It is the responsibility of the student to obtain any materials (i.e. lecture notes) from other students in the event the student cannot attend class for some reasons.†††

ē         All exercises must be turned in by the date the exercises are due.

o   Mostly, submission within 7 days of the due date will be considered as late submission. Each student will have one chance of late submission (within one week of the due date) with full score. Any second and more late submission within one week without advance permission by the instructor will cause a grade deduction. Students will get 75% of the point if submitting <=3 days late, 50% if submitting 4 to 7 days late. No exercise will be accepted after one week following the due date.By that time, the assignment answers will be handed out.

o   For the assignments right before mid-term and near the end of the semester, the late due date could be shortened (e.g., 3 days late due) or no late due allowed. The purpose is for me to give out the assignment answers earlier for mid-term preparation. In all, late due date printed on the assignment should be followed.

ē         Assignments are requested to submit mostly through Moodle. For the first two labs, hardcopy might be easier. Email attachments are not accepted. The instructor could grant extension for excused late submission.

ē         No make-up exams will be given unless PRIOR arrangements have been made with instructor and documentation of an illness is provided.

ē         Missing mid-term exam, presentations, final project presentation, project report, or >=3 homework will result in an F for the course regardless of the final grade. 

Disability Services and Accommodations

For disability accommodations, please register with Disability Services as early as possible. The registration must be done each semester. Donít expect the request will be automatically carried over.

Policy on class materials

Students can only use the notes they take from class for their own personal use, and not share (sell) these notes via an outside vendor or entity without the faculty/instructorís permission.This pertains to in-class recordings as well.Usage of the notes or in-class recordings in this way without the faculty memberís permission is a violation of the faculty memberís copyright protection.

This does not pertain to accommodations under the Americans with Disabilities Act (ADA), although recordings or sharing of Notes for ADA accommodations should not pertain to distribution beyond the students in the class receiving the accommodations.

Academic Honesty Policy

          All work submitted must be your own in presentation. You may discuss homework and final project with other students (you are encouraged to so), but the homework and project writeup must be your work.

          Exams are closed-book and no outside help is allowed. Any cheating on an exam will result in an F for the course.

          Copying is not allowed, and collaboration so close that it looks like copying is not allowed. Violation will be reported to the UMass Academic Honesty Board.

          If you make use of a printed or on-line source as reference, you need to cite it and mention it in your writeup.

 

Class Schedule (subject to change according to the progress)

This table will be updated through the semester. You can direct access by the following URL. This URL is also posted in Moodle. http://www.geo.umass.edu/courses/geo426/

 


Week

Class

Arrangement

Topic

Reading

1 Sept 2

Tu

Lecture 0

Introduction

 

 

 

No lab

 

Th

Lecture 1.1

Physic basis of remote sensing (1):

Electromagnetic radiation principles

Jensenís ch6 p185-191.

ParticleModel p191-196 is optional.

2 Sept 9

Tu

Lecture 1.2

Physic basis of remote sensing (2):

Atmospheric Energy-Matter interaction

Terrain Energy-Matter interaction(2)

Jensenís ch6

p196-205.

p208-215 (end before Atm Transmittance)

 

Lab 1

Hyperspectral curve, spectroradiometer

Jensenís ch 1, page 1- 3 In situ data collection

Th

Lecture 1.3

Physic basis of remote sensing: path radiance, atmosphere correction (3)

p220-223 (start from Absolute Atm Correction, end before Relative Radiometric Corr)

3 Sept 16

Tu

Lecture 2.1

Aerial photography: vantage point, cameras (1)

PDF in Moodle.

 

 

No lab

 

Th

Lecture 2.2

Aerial photography: color theory, filter (2)

PDF in Moodle, continue

4 Sept 23

Tu

Lecture 3.1

Multispectral remote sensing systems: concepts: digital images, resolution, orbits, platform, types of system

Jensenís ch 2 p37-38 (end before Microdensitometer) and p44-48, Ch 1 p3-17 (end before Polarization Information)

 

Lab 2

Stereo-airphoto interpretation

 

Th

Lecture 3.2

Multispectral remote sensing systems: Landsat and SPOT

Jensenís ch 2 p44-63 and p73-82

5 Sept 30

Tu

Lecture 3.2

ContÖ

 

Lab 3

Image display and multispectral remote sensing System

Th

Lecture 4.1

Thermal infrared remote sensing

PDF in Moodle

6 Oct 7

Tu

Lecture 4.2

Thermal infrared remote sensing

 

Lab 4

Thermal infrared remote sensing interpretation

Th

Lecture 3.3

Multispectral remote sensing systems -AVHRR, EOS, High resolution

Jensenís ch 2 p63-73, p88-101 (end before Airborne Digital Cameras)

7 Oct 14

Tu

 

Monday schedule, no class

 

 

 

Monday schedule, no class

Th

Lecture 5.1

Image enhancement

Jensenís ch 8 p273-301

8 Oct 21

Tu

 

No class, prepare mid-term exam

 

 

Mid-exam

 

Th

Lecture 5.2

Image enhancement, cont

Jensenís ch8 p308

9 Oct 28

Tu

Lecture 6.1

Information extraction:  supervised classification

Jensenís ch9 p361-402

 

Lab 5

Image enhancement

 

Th

Lecture 6.1

ContÖ

 

10 Nov 4

Tu

Lecture 6.2

Information extraction:  unsupervised classification and accuracy assessment

Jensenís ch9 p402-411

Lab 6

Classification

Jensenís ch12

Th

Lecture 6.3

Information extraction:  object-based classification

Jensenís ch9 p413-423

11 Nov 11

 

Tu

Lecture 7

Change detection

 Jensenís ch12

 

Lab 7

Object-based classification-eCognition

 

Th

 

Active Remote Sensing: Radar and Lidar

(Guest Lecturer: Prof. Paul Siqueira from ECE)

 

13 Nov 18

Tu

 

Final Project

 

 

Lab 8

Change detection

 

Th

 

Final project

 

12 Nov 25

 

 

Holiday

 

14 Dec 2

Tu

 

Working on final project

 

 

 

Working on final project

 

Th

 

Final project presentation

 

15 Dec 9

Tu

 

No class Final project report due on Dec 20