GEO-SCI426/626 Remote Sensing and Image Interpretation

Department of Geosciences

University of Massachusetts - Amherst

4 cr, Fall 2018

Class schedule table         Syllabus in PDF

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

Lecture†††††††††††††††† TuTh 10-11:15 pm    245 Morrill Sci. Ctr.


Qian Yu, Ph.D

Morrill 267


Office hour

TuTh 11:15-12:15 or by appointment

TA: Monica Weisenbach

Morrill IV 264


Office hour

Mon Wed 11:15-12:15 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

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


Class participation& attendance






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.







1 Sept 3


Lecture 0




Lecture 1.1

Physic basis of remote sensing (1):

Electromagnetic radiation principles

Jensenís ch6 p185-191.

ParticleModel p191-196 is optional.

2 Sept 10


Lecture 1.2

Physic basis of remote sensing (2):

Atmospheric Energy-Matter interaction

Terrain Energy-Matter interaction(2)

Jensenís ch6


p208-215 (end before Atm Transmittance)


Lab 1

Hyperspectral curve, spectroradiometer

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


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 17


Lecture 2.1

Aerial photography: vantage point, cameras (1)

PDF in Moodle.



No lab



Lecture 2.2

Aerial photography: color theory, filter (2)


4 Sept 24


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



Lecture 3.2

Multispectral remote sensing systems: Landsat and SPOT

Jensenís ch 2 p44-63 and p73-82

5 Oct 1





Lab 3 

Image display and multispectral remote sensing System



Lecture 3.3

Multispectral remote sensing systems -AVHRR, EOS, High resolution

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

6 Oct 8



Monday schedule, no class


Lecture 4.1

Thermal infrared remote sensing

PDF in Moodle

7 Oct 15


Lecture 4.2

Thermal infrared remote sensing



Lab 4

Thermal infrared remote sensing interpretation


Lecture 5.1

Image enhancement

Jensenís ch 8 p273-301

8 Oct 22


Lecture 5.2

Image enhancement, cont

Jensenís ch8 p308


No lab






9 Oct 29


Lecture 6.1

Information extraction:  supervised classification

Jensenís ch9 p361-402


Lab 5

Image enhancement



Lecture 6.2

Information extraction:  unsupervised classification

Jensenís ch9 p402-411

10 Nov 5


Lecture 6.3

Information extraction:  object-based classification

Jensenís ch9 p413-423

Lab 6




Lab 7

Change detection

Jensenís ch12

11 Nov 12



Lecture 8

Object-based classification



Lab 8

Object-based classification




Working on final project


12 Nov 19





13 Nov 26



Working on final project




Working on final project




Working on final project


14 Dec 3



Working on final project




Working on final project




Final project presentation


15 Dec 10



No class




Final project report due on Dec 20