Remote Sensing and Radiometry

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.

Learning Outcomes

  1. Describe remote sensing applications
  2. Review electromagnetic scattering and applications
  3. Combine electromagnetic waves and signal processing ideas
  4. Review Inverse Synthetic-Aperture radar and Synthetic-Aperture radar ideas
  5. Explain main image reconstruction algorithms implementations
  6. Use main image reconstruction algorithms
  7. Compare main image reconstruction algorithms and their implementations

Forms of Teaching

Lectures

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.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Homeworks 0 % 100 % 0 % 0 %
Exam: Written 0 % 50 %
Exam: Oral 50 %

Week by Week Schedule

  1. Introduction to remote sensing. Electromagnetic waves and electromagnetic spectrum applicable in remote sensing.
  2. Wave propagation. Planar electromagnetic wave. Attenuation, reflection, transmission and polarization.
  3. Practical aspects of electromagnetic radiation measurements.
  4. Electromagnetic wave scattering. Green's function. Lippmann-Schwinger equation.
  5. Practical methods for scattering detection and evaluation.
  6. Applications of asymptotic analysis in electromagnetic wave scattering. Stationary phase method and Born approximation.
  7. Detection of a wave scattered from a stationary and a nonstationary object.
  8. Submission of homework assignments.
  9. Inverse Synthetic Aperture Radar (ISAR) and applications.
  10. Synthetic aperture (SAR) radar and applications.
  11. Laboratory realization of synthetic aperture radar.
  12. Image reconstruction in synthetic aperture radar.
  13. Algorithms for image reconstruction in synthetic aperture radar.
  14. Application of algorithms for image reconstruction on the results obtained by laboratory measurements.
  15. Submission of homework assignments.

Study Programmes

University graduate
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Literature

Margaret Cheney, Brett Borden (2009.), Fundamentals of Radar Imaging, SIAM
Mark A. Richards (2013.), Fundamentals of Radar Signal Processing, Second Edition, McGraw Hill Professional

For students

General

ID 222515
  Winter semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures

Grading System

85 Excellent
75 Very Good
60 Good
50 Acceptable