GEO-SCI426/626 Remote Sensing and Image Interpretation

Department of Geosciences

University of Massachusetts - Amherst

4 cr, Fall 2017

Class schedule table         Syllabus in PDF

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

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

Instructor††††††††††††

Qian Yu, Ph.D

Morrill 267

qyu@geo.umass.edu

 

Office hour

Mon 11-12,Th 11:15-12:15 or by appointment

TA: Jiwei Li

Morrill IV 264

jiweili007@gmail.com

 

Office hour

Wed 10-11 or by appointment

†††††††††††††††

Course description

This course provides an introduction to 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 purpose

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.

Course objectives

This class will insure 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 user's 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% 

30%

25% 

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:

      Attendance to both lecture and lab is required in the normal circumstances and forms a portion of your grade. Failure to meet course requirements due to illness will require documentation for alternate arrangements to be made. It is the responsibility of the student to obtain any materials (i.e. notes) from other students in the event the student cannot attend class for some reasons.   

      All exercises must be turned in by the date the exercises are due.  Submission within one week 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 consecutive late submission within one week without advance permission by the instructor will cause a grade deduction by half. No exercise will be accepted after one week following the due date.

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

      Missing mid-term exam, or final exam/project presentation, or >=3 homework will result in an F for the course.

 

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 4

Tu

Lecture 0

Introduction

 

Th

Lecture 1.1

Physic basis of remote sensing (1): Electromagnetic radiation principles

Jensenís ch6 p185-191.

ParticleModel p191-196 is optional.

2 Sept 11

Tu

Lecture 1.2

Physic basis of remote sensing (2):

         Atmospheric energy-matter interaction

         Terrain energy-matter interaction

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 (3):

         Radiance and path radiance

         Atmospheric correction

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

3 Sept 18

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)

 

4 Sept 25

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)

Ch 1 p3-17 (end before Polarization Information)

 

Lab 2

Stereo-airphoto interpretation and mosaic

 

Th

Lecture 3.2

Multispectral remote sensing systems: Landsat and SPOT

Jensenís ch 2 p44-63 (start from Digital Remote Sensor Data Collection)

p73-82

5 Oct 2

Tu

Lecture 3.2

ContinueÖ

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

Lab 3 

Image display and multispectral remote sensing System

 

Th

Lecture 3.3

Multispectral remote sensing systems -AVHRR, EOS, High resolution, Hyperspectal

6 Oct 9

Tu

 

Monday schedule, no class

Th

Lecture 4.1

Thermal infrared remote sensing

Pdf in moodle

7 Oct 16

Tu

Lecture 4.2

Thermal infrared remote sensing

 

Lab 4

Thermal infrared remote sensing interpretation

Th

Lecture 5.1

Image enhancement

Jensenís ch 8 p273-301

8 Oct 23

Tu

Lecture 5.2

Image enhancement

No lab

 

Th

 

Mid-term exam

 

9 Oct 30

Tu

Lecture 5.3

Image enhancement, cont,

Jensenís ch8 p308

 

Lab 6

Image enhancement

 

Th

Lecture 8.1

Information extraction:  classification

 

10 Nov 6

Tu

Lecture 8.2

Information extraction:  classification

 

Lab 7

Classification

 

Th

 

No class

 

11 Nov 13

Tu

Lecture 9

Change detection

 

 

Th

Lab 9

Change detection

 

12 Nov 20

 

 

Holiday

 

13 Nov 27

Tu

 

Working on final project

 

 

 

Working on final project

 

Th

 

Working on final project

 

14 Dec 4

Tu

 

Working on final project

 

 

 

Working on final project

 

Th

 

Final project presentation

 

15 Dec 11

Tu

 

No class, work on final project report on your own

 

16 Dec 18

 

 

Final project report due