ECE 7402
Transcript Abbreviation:
MACH LEARN REM SEN
Course Description:
Introduces basic high-resolution remote sensing concepts with focus on optical remote sensing, and computational solutions for typical remote sensing problems. Students are assumed to know basic knowledge in statistical estimation, linear algebra and numerical methods for engineering. Students with prior experiences on remote sensing/machine learning/computer vision will find this easier to follow
Course Levels:
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
Cross-listed in CivilEn 7421.
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Grad standing, or permission of instructor.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for CivilEn 7421.
Course Goals / Objectives:
The students understand both the geometric and spectrum characteristics of common remote sensing data
The students understand the basic concepts in statistical learning and deep learning.
The students should be able to design algorithmic flows for typical remote sensing problems such as object detection, semantic segmentation, and change detection, and will be able to implement their own system with state of the art packages.
Students will be able to understand image formation, spectral analysis, segmentation, and machine learning based object recognition.
Students will be able to incorporate relevant knowledge into their Ph.D. or Master Research topics.
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Introduction to remote sensing | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Image geometry and spectroscopy | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Spectroscopic analysis with remote sensing images | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Statistical learning, SVM and applications to land cover classification of remote sensing images (optical and SAR) | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Decision-tree and random forest, for application of landcover classification of low-to-high resolution satellite images | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Neural networks and backpropagation | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Feature engineering using remote sensing multispectral images remote sensing linear indices | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Feature Engineering Spatial and geometric features on remote sensing dataset | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Deep learning for semantic segmentation of satellite images | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Common architectures for satellite images | 3.0 | 0.0 | 0 | 0.0 | 0 |
| GAN for remote sensing image synthesis and super-resolution | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Deep learning for multi-source data fusion | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 36 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 70% |
| Final project | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No Textbooks and Other Course Materials Entered. | ||
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: