PristupačnostRemote Sensing
Data is displayed for the academic year: 2025./2026.
Lectures
Laboratory exercises
Course Description
Remote sensing is the term for a set of methods for gathering information about an object or phenomenon without physical contact with that object. Some of the applications of remote sensing are imaging the Earth from space, deep probing of the ocean, monitoring the effect of climate change on glaciers, ultrasound pregnancy monitoring, and the most famous technologies are radar, lidar, MRI (Magnetic Resonance Imaging), PET (Positron Emission) Tomography) and many others. The advantages of remote sensing are that it does not disturb the object or the area of observation, and it also enables measurements in remote, inaccessible and dangerous areas. Remote sensing connects different fields of electrical engineering, computing and the field of mathematical modeling with the aim of improving existing methods and developing future innovative applications. The course will be conducted by combining theoretical and practical aspects of remote sensing with the aim of better connecting and understanding new concepts and technologies. In this way, the knowledge of mathematical modeling, signal processing and electromagnetism will be connected and extended, with a focus on the development and demonstration of microwave imaging and synthetic aperture radar ideas, and the application of adequate algorithms for image reconstruction on practical examples.
Study Programmes
University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses
(1. semester)
(3. semester)
[FER3-HR] Communication and Space Technologies - profile
Core-elective courses
(1. semester)
[FER3-HR] Computer Engineering - profile
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[FER3-HR] Computer Science - profile
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[FER3-HR] Control Systems and Robotics - profile
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[FER3-HR] Data Science - profile
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[FER3-HR] Electrical Power Engineering - profile
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[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses
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[FER3-HR] Electronic and Computer Engineering - profile
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(1. semester)
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[FER3-HR] Electronics - profile
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(1. semester)
(3. semester)
[FER3-HR] Information and Communication Engineering - profile
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(1. semester)
(3. semester)
Elective Courses of the Profile
(1. semester)
(3. semester)
[FER3-HR] Network Science - profile
Elective Courses
(1. semester)
(3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
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(1. semester)
(3. semester)
Learning Outcomes
- Explain the main applications of remote sensing.
- Combine knowledge from signal processing with electromagnetic wave phenomena.
- Relate the physical foundations of wave scattering modeling to applications in object detection.
- Explain the operating principle of synthetic aperture radar (SAR).
- Apply radar image reconstruction algorithms to measured data.
- Compare different image reconstruction algorithms and understand their advantages and disadvantages.
Forms of Teaching
Lectures
-
Independent assignments-
Laboratory-
Grading Method
| Continuous Assessment | Exam | |||||
|---|---|---|---|---|---|---|
| Type | Threshold | Percent of Grade | Threshold | Percent of Grade | ||
| Homeworks | 0 % | 40 % | 0 % | 0 % | ||
| Seminar/Project | 50 % | 30 % | 0 % | 0 % | ||
| Mid Term Exam: Written | 25 % | 30 % | 0 % | |||
| Exam: Written | 25 % | 50 % | ||||
| Exam: Oral | 50 % | |||||
Week by Week Schedule
- Introduction to remote sensing.
- Fourier series and applications in signal processing.
- Fourier transform and spectrum.
- Analytic signal. Hilbert transform. IQ demodulation process.
- Stationary phase method and applications.
- Discrete and Fast Fourier Transform (FFT).
- Introduction to wave-based detection. Detection of stationary and moving objects.
- Midterm.
- Doppler radar imaging and velocity estimation of an object.
- Effect of noise and pulse compression. Correlation receiver.
- FMCW radar.
- Synthetic aperture radar image model and the omega-k algorithm.
- Implementation of the omega-k algorithm.
- Practical applications of the omega-k algorithm.
- Project.
Literature
Dario Bojanjac, Marko Bosiljevac, Filip Turčinović (2025.), Kako snimiti i rekonstruirati SAR sliku, skripta
Margaret Cheney, Brett Borden (2009.), Fundamentals of Radar Imaging, SIAM
Mark A. Richards (2013.), Fundamentals of Radar Signal Processing, Second Edition, McGraw Hill Professional
General
ID 240624
Winter semester
5 ECTS
L1 e-Learning
45 Lectures
0 Seminar
0 Exercises
6 Laboratory exercises
0 Project laboratory
0 Physical education excercises
Grading System
85 Excellent
75 Very Good
60 Good
50 Sufficient