Title of course: Multispectral remote sensing
Code: TTGMG7046_EN
ECTS Credit points: 3
Type of teaching, contact hours
- lecture: 1 hours/week
- practice: 2 hours/week
- laboratory: -
Evaluation: practical grade
Workload (estimated), divided into contact hours:
- lecture: 14 hours
- practice: 28 hours
- laboratory: -
- home assignment: -
- preparation for the exam: 16 hours
Total: 30 hours
Year, semester: 1st year, 1 st semester
Its prerequisite(s): -
Further courses built on it: -
Topics of course
The aim of the course is to learn the theoretical and practical aspects of multispectral remote sensing, widely used in geography, using databases with different sensors in different types of land cover. The theoretical part of the course will cover the following topics: main characteristics of the electromagnetic spectrum; electromagnetic spectral intervals used in remote sensing; types of remote sensing sensors; properties of typical sensor systems; main characteristics of raster databases; steps of processing remote sensing databases; preprocessing of space images; main types of classifications in remote sensing; automatic and unsupervised classifications; main characteristics of supervised classifications; object-based classifications; additional processing techniques with space images (filters, palettes, etc.). The practical part of the course will cover the following topics: study of the main features of the electromagnetic spectrum on surface objects, calculation of their effects; study of spectral ranges used in remote sensing, atmospheric windows; operations with raster-based databases; preprocessing of space images, main methods and steps; methods and parameterisation issues of unsupervised classification (K-mean, ISODATA, ISOCLUSTER); semi-automatic classification procedure; methods and parameterisation issues of supervised classification (Maximum Likelihood, Support Vector Machine, Random Forest, k-Neareaset Neioghbour); object-based classification (GEOBIA). Cloud-based image processing using Geoogle Earth Engine.
Literature
- Campbell, J. (1996). Introduction to remote sensing (2nd ed.). New York: Guilford Press
- Congedo, L. (2021) Semi-Automatic Classification Plugin Documentation Release 7.8.0.1 https://readthedocs.org/projects/semiautomaticclassificationmanual/down…;
- Hadjimitsis, D. ed. (2013) Remote Sensing of Environment. IntechOpen https://www.intechopen.com/books/remote-sensing-of-environment-integrat…;
- Schowengerdt, R. A. (2007). Remote sensing: Models and methods for image processing. S.l.: Academic Pr.
- Lillesand, T., Kiefer, R. (1994). Remote sensing and image interpretation (3rd ed.). New York: Wiley and Sons
Requirements:
- for a signature
Attendance at lectures is recommended, but not compulsory.
- for a grade
The course ends in a writing examination. The minimum requirement for the test respectively is 50%. Based on the score of the test, the grade for the test is given according to the following table:
| Score | Grade |
| 0-49 fail | (1) |
| 50-64 pass | (2) |
| 65-74 satisfactory | (3) |
| 75-85 good | (4) |
| 86-100 excellent | (5) |
If the score of any test is below 50, students can take a retake test in conformity with the EDUCATION AND EXAMINATION RULES AND REGULATIONS.
Person responsible for course: Dr. Zoltán Krisztián Túri, PhD, Assistant Professor