GEO-SCI426/626
Remote Sensing and Image Interpretation
Department of Earth, Geographic, and Climate
Sciences
University
of Massachusetts - Amherst
4
cr, Fall 2024
Notes, lab and assignments will be updated on the Canvas.
Lecture TuTh 1-2:15 pm 225 Morrill Sci. Ctr.
Lab Tu
2:30-4:30 pm 212 Morrill II (UMass IT lab)
(No lab on 9/5. First lab on 9/12)
Instructor Prof. Qian Yu, Morrill 267, qyu@umass.edu, Office hour: Mon 1:30-2:30, Thu 2:30-3:30 or by appt
TA Dylan Roy, Morrill 264, djroy@umass.edu, Office hour: Wed 10-11, Friday
1:30-2:30 or by appt
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 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 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 Geometry, Algebra 2, and precalculus.
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.
* Digital,
hardcopy, any format is fine. The book is on reserve in the library.
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,
Kiefer and Chipman, Remote Sensing and Image Interpretation, Wiley.
4.
Sabins, Floyd and James Ellis. Remote Sensing: Principles, Interpretation,
and Applications,
Waveland Press.
* Reference books are not part of the reading. They are
listed as courtesy as some of my lecture figures are from those sources.
Lab
software access
A professional
remote sensing software ENVI will be used for most of the labs. ENVI is
installed in 212 Morrill. You can also access ENVI through UMass Azure Virtual
Desktop. You can find the instruction on how to access AVD here.
https://www.umass.edu/it/computer-classrooms/windows-virtual-desktop
Grading
and evaluation
Assignments
and labs Mid-Term Exam Quiz Final Project Sensor/Paper Presentation Class
participation& attendance |
30% 25% 8% 25% 7% 5% |
-
All written assignments must be handed
in on time.
-
Mid-exams will cover key concepts from lecture, readings and
remote sensing theories.
-
Presentation:
426
students will present a satellite sensor.
626 students will present a journal paper about
remote sensing application in your field.
-
Attendance will be accessed by class
participation, taking rolls at random dates and pop quiz.
Policy on attendance and due dates for assignments:
·
The class is taught
in person with zoom recording for lectures!
426 students must
take the course in-person. For most students taking the class in person, attendance
to both lecture and lab session is required in the normal circumstances and
forms a portion of your grade. Lectures and some lab tutorials will be recorded
for the convenience of review. This does not mean you are free to skip lectures
or labs. For absences due to health reasons, family illness, religious
observance or other extenuating circumstances, students should fill out a
google form to record excused absences in a timely fashion. Verification
document is requested for alternate arrangements to be made. It is the
responsibility of the student to review the lecture recording in the event the
student cannot attend class.
https://forms.gle/C5gQa2mjMb4P2h4r7
Most 626 students take the course in person as well. The class is also
open to 626 students who enrolled in GIST Masters
program online and need to take the course by zoom live stream or
asynchronously. Please fill out the pre-class survey and get approval from the
instructor at the beginning of the semester. We also want to know how to help your remote learning. A student must declare you want to take the course fully
online or fully in person.
· Assignment late submission policy Both due date and late due date are noted in each assignment. The late due date is normally 7 days after the due date. 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.
All
exercises must be turned in by the due date for full credit unless arranged with the instructor before
the deadline. Each student has one chance of late submission
(before late due date) with no point deduction during the semester. Other late
submissions before late due date without advance permission by the instructor
will cause a grade deduction. No lab
assignment will be accepted after late due date. By that time,
the assignment answers will be released.
Late submission |
1-3 days |
4-7 days |
>7 days or after late due date |
Point deduction |
1/4 |
1/2 |
Not accepted, the assignment
answers will be released |
The instructor
could grant extension for excused late submission. Please email the instructor
to request an extension.
·
Assignments
should be submitted through Canvas. Any submission made by email
attachment or link to your shared document will not be considered for grading
and meeting deadline.
·
Mid-term is close book and the schedule on
syllabus is only tentative. No make-up exams will be given unless PRIOR
arrangements have been made with instructor and documentation of excused must
be provided.
·
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 Accommodations
The University of Massachusetts Amherst is committed
to providing an equal educational opportunity for all students. If you have a
documented physical, psychological, or learning disability on file with
Disability Services (DS), you may be eligible for reasonable academic
accommodations to help you succeed in this course. For disability
accommodations, please register with Disability Services as early as possible.
Meanwhile please notify the instructor through the pre-class survey or email
within the first two weeks of the semester so that we may make appropriate
arrangements. DS accommodation request must be filed each
semester. Do not expect the request will be automatically carried over.
Policy on class materials and recordings
Students can only use the lecture notes and
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/instructor’s
permission. This pertains to in-class
recordings as well. Usage of the notes
or in-class recordings in this way without the faculty member’s permission is a
violation of the faculty member’s 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
Since the integrity of the academic enterprise of any
institution of higher education requires honesty in scholarship and research,
academic honesty is required of all students at the University of Massachusetts
Amherst. Academic dishonesty is prohibited in all programs of the University.
Academic dishonesty includes but is not limited to:
cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate
sanctions may be imposed on any student who has committed an act of academic
dishonesty. (http://www.umass.edu/dean_students/codeofconduct/acadhonesty/).
·
All work submitted must be your own in
presentation. You may discuss homework and final project with other students
(you are encouraged to do so), but the homework and project write-up must be
your work.
·
Copying homework 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.
·
In write-up, if you make use of a printed or
on-line source as reference, you need to cite it and mention it.
Class Schedule (subject to change according to the progress)
This table will be updated throughout the
semester. Schedule could change according to our progress (which is why they
are in grey) and reading will be filled in. You can
direct access by the following URL (no canvas login required). This URL is also
posted on Canvas.
http://www.geo.umass.edu/courses/geo426/
Week |
Class |
Arrangement |
Topic |
Reading |
1 Sept 2 |
Tu |
Lecture 0 |
Introduction and syllabus |
|
|
No lab |
|
||
Th |
Lecture 1.1 |
Physic basis of remote sensing (1): Electromagnetic radiation principles |
Jensen’s ch6 p185-191. Particle Model 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) Physic basis of remote sensing: path
radiance, atmosphere correction (3) This part will be covered in Lab 3 |
Jensen’s ch6 p196-205. p208-215 (end before Atm
Transmittance) p220-223 (start from
Absolute Atm Correction, end before Relative Radiometric Corr) |
Lab 1 |
Hyperspectral curve,
spectroradiometer |
Jensen’s ch 1, page 1- 3 In situ data collection |
||
Th |
Lecture
2.1 |
Aerial photography: vantage point,
cameras (1) |
PDF in Canvas. |
|
3 Sept 16 |
Tu |
Lecture
2.2 |
Aerial photography: color theory,
filter (2) |
|
Lab 2 |
Aerial
Photographic System |
|
||
Th |
Lecture
3.1 |
Multispectral
remote sensing systems: concepts: digital images, resolution, orbits,
platform, types of system |
Jensen’s ch 2 p37-38 (end before
Microdensitometer) and p44-48, Ch 1 p3-17 (end before Polarization
Information) |
|
4 Sept 23 |
Tu |
Lecture
3.2 |
Multispectral
remote sensing systems: Landsat and SPOT |
Jensen’s ch 2 p44-63 and p73-82 |
|
No lab |
|
||
Th |
Lecture
3.2 |
Cont, |
|
|
5 Sept 30 |
Tu |
Lecture
3.3 |
Multispectral
remote sensing systems -AVHRR, EOS, High resolution |
Jensen’s 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- SmallSat, hyperspectral |
|
|
7 Oct 7 |
Tu |
Lecture 4.1 |
Thermal infrared remote sensing |
Online reading |
Lab 3b |
Access public remote sensing data and
preprocessing |
|
||
Th |
Lecture 4.2 |
Thermal infrared remote sensing |
|
|
8 Oct 14 |
Tu |
|
No class, Monday schedule |
|
|
No class, Monday schedule |
|
||
Th |
Lab 4 |
Thermal infrared remote
sensing interpretation |
PDF in Canvas. |
|
9 Oct 21 |
Tu |
Lecture 5.1 |
Image enhancement: radiometric enhancement |
Jensen’s ch 8 p273-301 |
Lab 4 |
Cont, |
|
||
Th |
Lecture 5.2 |
Image enhancement: spatial enhancement |
Jensen’s ch8 p308 |
|
10 Oct
28 |
Tu |
Lecture 5.3 |
Image enhancement: spectral enhancement |
|
Lab 5 |
Image enhancement |
|
||
Th |
Lecture 6.1 |
Information extraction: supervised
classification |
Jensen’s ch9 p361-402 |
|
11 Nov 4 |
Tu |
|
Holiday, no class |
|
|
Holiday, no lab |
|
||
Th |
|
Mid-term exam |
|
|
12 Nov
11 |
Tu |
Lecture 6.2 Lecture 6.3 |
Information extraction: unsupervised
classification Information extraction: object-based
classification |
Jensen’s ch9 p402-411 Jensen’s ch9 p413-423 |
|
Lab 6 |
Image Classification |
|
|
Th |
|
Sensor
presentations |
|
|
13 Nov
18 |
Tu |
Paper
presentations |
|
|
Lec &
Lab 7 |
Change detection |
Jensen’s ch12 |
||
Th |
Final
project |
|
||
14 Nov
25 |
Tu |
|
Final project |
|
|
|
Final
project |
|
|
Th |
|
Thanksgiving
recess |
|
|
15 Dec 2 |
Tu |
|
Working
on final project |
|
|
|
|
Working
on final project |
|
|
Th |
|
Working
on final project |
|
16 Dec 9 |
Tu |
|
Final
project presentation |
|
|
|
|
Final project report due on Dec 18 |
|