Courses
|
Academic Year |
Semester |
Course Name |
Department |
|
2010-2011 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Principles of Remote Sensing |
GGIT |
|
|
2011-2012 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Digital Image Analysis |
GGIT |
|
|
2012-2013 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Digital Image Analysis |
GGIT |
|
|
2013-2014 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Digital Image Analysis |
GGIT |
|
|
2014-2015 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Digital Image Analysis |
GGIT |
|
|
2015-2016 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Digital Image Analysis |
GGIT |
|
|
2016-2017 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Digital Image Analysis |
GGIT |
|
|
2017-2018 |
Autumn |
Principles of Remote Sensing |
GGIT |
|
Spring |
Digital Image Analysis |
GGIT |
GGIT 560 Principles of Remote Sensing
-
- Objectives
-
- This course is for students who have no previous experience in remote sensing, aiming to initiate students to the fundamentals of remote sensing by providing the theory and hands-on experience with real data.
-
- Outline
-
- Week 1. Introduction: Definition of RS, history of RS, application examples, visual image interpretation
-
- Week 2. Radiation and its interaction with matter: Electromagnetic spectrum, black body radiation, atmosphere, scattering, absorption, transmittance, interaction with surface, BRDF, albedo, spectral signature, radiometric terms
-
- Week 3. Basics of photography and imaging: camera obscura, image formation, focus, depth of field, exposure, aperture, aberrations, color, visualization, color filters, film, digital images, CCD, CMOS
-
- Week 4. Resolution: spectral resolution, spatial resolution, diffraction limit, PSF, MTF, radiometric resolution, SNR
-
- Week 5. Satellite platforms & imaging: satellite basics, orbits, temporal resolution, frame camera, whiskbroom, pushbroom
-
- Week 6. Imagers: aerial cameras, satellite cameras, active systems, comparison, example images, thermal imaging, hyperspectral imaging
-
- Week 7. Midterm exam
-
- Week 8. Introduction to image analysis: signals, time and frequency domain Preprocessing: radiometric correction
-
- Week 9. Preprocessing: elementary geodesy concepts, GPS, georeferencing, geometric correction
-
- Week 10. Image enhancement: histogram operations, spatial filtering
-
- Week 11. Multi-band operations: color-spaces, principal component analysis
-
- Week 12. Classification: supervised and unsupervised classification, accuracy assessment
-
- Week 13. RADAR, LIDAR
-
- Week 14. Term Project Presentations
GGIT 561 Digital Image Analysis
-
- Objectives
-
- This course is designed to introduce principles and applications of image processing for remote sensing and provide hands-on experience with real data to graduate students with basic knowledge of mathematics and computer programming.
-
- Outline
-
- Week 1. Introduction. Remotely sensed images.
-
- Week 2. Error correction and registration.
-
- Week 3. Image Interpretation. Radiometric Enhancement.
-
- Week 4. Geometric Enhancement.
-
- Week 5. Multispectral transformations.
-
- Week 6. Fourier transformation.
-
- Week 7. Midterm exam.
-
- Week 8. Supervised classification.
-
- Week 9. Unsupervised classification.
-
- Week 10. Feature reduction.
-
- Week 11. Classification strategies.
-
- Week 12. Image fusion.
-
- Week 13. Hyperspectral image processing.
-
- Week 14. Presentations of students.
GGIT 765 Optical Imaging for Remote Sensing
-
- Objectives
-
- understand the principals of optical image formation from light sources to digital numbers in images
- able to establish elaborate models of image formation
- able to design imagers at the system level can translate remote sensing needs into technical requirements for imaging systems
- contribute to system engineering activities of imager design projects
PETE 751 Remote Sensing Applications in Petroleum Engineering
-
- Objectives
-
- This course, which is intended for petroleum and natural gas engineering graduate students who have no previous experience in remote sensing, aims 1) to introduce the fundamentals of remote sensing and related disciplines, 2) to exemplify the use of remote sensing and Geographic Information Systems for applications in petroleum engineering topics. As part of the course, the students are expected to learn MATLAB and GRASS at basic level.
PETE 557 Analysis of Porous Media Flow Equations II
-
- Objectives
-
- This course, which is intended for petroleum and natural gas engineering graduate students, will focus on the mathematical background necessary to solve partial differential equations of unsteady state flow in porous media and the application of these techniques for solving the basic equations frequently used in petroleum engineering. The students will acquire the skills for analytical solutions of transients encountered in well testing.