ERE692 REMOTE SENSING OF THE ENVIRONMENT
Fall 2011
Instructor: Dr. Jungho Im
Email: imj@esf.edu
Office: Baker Lab #417
Office hours: M 1 – 2 W 12:30 – 1:30
TA: Mr. Zhenyu Lu
Email: zlu03@syr.edu
Office hours: TBA
Lecture: W 1:50 – 4:50 at Baker 434
Texts
 Jensen, J.R., 2007, Remote Sensing of the Environment: An Earth Resource
Perspective, Upper Saddle River, NJ: Prentice Hall, 2nd Ed., 592 pages.
 Jensen, J. R., 2005, Introductory Digital Image Processing: A Remote Sensing
Perspective, Upper Saddle River, NJ: Prentice Hall, 3rd Ed., 526 pages.
Purpose of Course
This course investigates diverse applications of remote sensing as well as advanced digital
image processing techniques for each application. This course covers understanding of various
remote sensing systems (e.g. hyperspectral, LiDAR), their applications (e.g. vegetation, water)
and advanced digital image processing techniques (e.g. object-based, machine learning, artificial
immune networks). Several interactive digital image processing systems (e.g., ENVI, ERDAS
IMAGINE, ArcGIS, and/or Matlab) are used by the students to analyze satellite and airborneacquired remotely sensed image data.
Expected Outcomes:
 Students will learn various remote sensing systems and how they can be used for each
field of study.
 They will learn diverse digital image processing techniques and how to apply them to real
world remote sensing data to extract meaningful information.
 Students will work on individual projects to practice their knowledge and analytical
techniques that they have obtained during the course.
Prerequisite: Students who are entering this course must have basic knowledge of remote
sensing and digital image processing. FEG365 – Principles of Remote Sensing is a prerequisite
for undergraduate students.
Grading:
 Labs (36%): there will be approximately 12 lab assignments. Labs are due the following
week during lab hours. Late labs are assessed a 5% penalty for each day tardy. Note:
copying classmates’ work is not permitted!
 Quizzes (10%): there will be approximately 5 quizzes. No makeups.
 Exams (34%): there will be two exams (17% each).
 Term Project (20%): each student is expected to conduct an independent project. It
consists of presentation (20%) and paper (80%). A more detailed guideline will be
distributed later.
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Tentative Course Schedule
Week
Week 1 (Aug 31)
Week 2 (Sep 7)
Week 3 (Sep 14)
Week 4 (Sep 21)
Week 5 (Sep 28)
Week 6 (Oct 5)
Week 7 (Oct 12)
Week 8 (Oct 19)
Week 9 (Oct 26)
Week 10 (Nov 2)
Week 11 (Nov 7)
Week 12 (Nov 16)
Week 13 (Nov 23)
Week 14 (Nov 30)
Week 15 (Dec 7)
Week 16
Topics
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Course overview
Multispectral remote sensing (high spatial resolution)
Object-based analysis
Lab#1: Image segmentation and object-based image classification
Thermal infrared remote sensing and applications
Forward-looking infrared systems
Lab #2: Urban heat island analysis using thermal remote sensing
Hyperspectral remote sensing
Subpixel mapping and hyperspectral regression
Lab #3: Land cover mapping using hyperspectral image datacube
LiDAR remote sensing
LiDAR data processing and applications
Lab #4: Vertical accuracy of LiDAR data and LiDAR-derived feature
metrics
LiDAR data classification
Lab #5: LiDAR data classification (bare Earth and buildings extraction)
Remote sensing vegetation
Hyperspectral vegetation indices
Lab #6:Phenological cycle and biophysical characteristics of vegetation
in situ data collection
Laboratory reflectance vs. in situ reflectance
Midterm exam
Exam review
Discussion on the term projects
Spectroradiometer, Ceptometer, and clinometer
Lab #7:Spectroradiometer measurements
Remote sensing water
Bathymetry
Lab #8:Snow and cloud exploration using multispectral remote sensing data
Project outline presentation
Remote sensing the urban landscape
Lab #9: Population estimation using LiDAR and optical sensor data
Remote sensing change detection
Lab #10:Classification vs. calibration approaches for change detection
Advanced information extraction I
Neural networks and decision trees
Lab #11:Advanced image classification I
Thanksgiving week (No class)
Advanced information extraction II
Support vector machine and artificial immune networks
Lab #12:Advanced image classification II
Final exam
Term project (help session)
Presentation (15 min per person)
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