Basic information
- Field of study
- Geodesy, Surveying and Cartography
- Major
- Geoinformation, Photogrammetry and Remote Sensing
- Organisational unit
- Faculty of Geo-Data Science, Geodesy, and Environmental Engineering
- Study level
- Second-cycle (engineer) programme
- Form of study
- Full-time studies
- Profile
- General academic
- Didactic cycle
- 2024/2025
- Course code
- DGIKGFS.IIi4.01633.24
- Lecture languages
- English
- Mandatoriness
- Elective
- Block
- Elective Modules in Foreign Language
- Course related to scientific research
- Yes
|
Period
Semester 3
|
Method of verification of the learning outcomes
Completing the classes
Activities and hours
Project classes:
30
|
Number of ECTS credits
3
|
Goals
| C1 | The aim of the module is to introduce students to hyperspectral remote sensing techniques and the possibilities of applying these methods to various land cover/land use monitoring tasks. |
Course's learning outcomes
| Code | Outcomes in terms of | Learning outcomes prescribed to a field of study | Methods of verification |
| Knowledge – Student knows and understands: | |||
| W1 | has knowledge about hyperspectral images processing | GIK2A_W02, GIK2A_W05, GIK2A_W06 | Activity during classes, Participation in a discussion, Execution of a project, Project, Involvement in teamwork, Presentation |
| W2 | has knowledge about hyperspectral data acquiring and application | GIK2A_W05, GIK2A_W06, GIK2A_W07 | Activity during classes, Participation in a discussion, Execution of a project, Project, Involvement in teamwork, Presentation |
| Skills – Student can: | |||
| U1 | has ability to hyperspectral image processing | GIK2A_U03, GIK2A_U04, GIK2A_U08, GIK2A_U09 | Activity during classes, Participation in a discussion, Execution of a project, Project, Involvement in teamwork, Presentation |
| Social competences – Student is ready to: | |||
| K1 | has ability to acquire a new knowledge | GIK2A_K01 | Activity during classes, Participation in a discussion, Execution of a project, Project, Involvement in teamwork, Presentation |
Program content ensuring the achievement of the learning outcomes prescribed to the module
The module is designed to provide knowledge about the possibilities of remote sensing applications in solving tasks carried out by various fields. In the module, the student will learn about hyperspectral data registered from ground, airborne and satellite levels. Students will learn to use hyperspectral remote sensing data in monitoring selected elements of the surrounding.
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Project classes | 30 | |
| Realization of independently performed tasks | 10 | |
| Contact hours | 5 | |
| Preparation of project, presentation, essay, report | 30 | |
| Examination or final test/colloquium | 2 | |
| Student workload |
Hours
77
|
|
| Workload involving teacher |
Hours
30
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
Preprocessing of hyperspectral data: Initial correction of hyperspectral images: correction of atmospheric and topographical effect. Radiometric correction. Terrestrial hyperspectral data processing. |
W1, W2, U1, K1 | Project classes |
| 2. |
Data extraction: Image classification, applying special methods and algorythms dedicated hyperspactral image processing basing on the in-situ spectrometer data. |
W1, W2, U1, K1 | Project classes |
Extended information/Additional elements
Teaching methods and techniques :
Discussion, E-learning, Case study, Problem Based Learning, Project Based Learning
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Project classes | Activity during classes, Participation in a discussion, Execution of a project, Project, Involvement in teamwork, Presentation |
Conditions and the manner of completing each form of classes, including the rules of making retakes, as well as the conditions for admission to the exam
Project exercises include the training of practical application of the knowledge and are mandatory. Credit is based on student’s activity.
Method of determining the final grade
final score = score of project
Manner and mode of making up for the backlog caused by a student justified absence from classes
Compensating for the backlog caused by absence: depending on the classes subject – self-realisation of excercises with the help of individual consultations with the instructor.
Prerequisites and additional requirements
basic knowledge of remote sensing
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Project classes: Project exercises: Students carry out practical work aimed at achieving the competences assumed by the syllabus. The manner in which the project is carried out and the final result are assessed.
Literature
Obligatory- ENVI Tutorial
- Hyperspectral Imaging http://www.microimages.com/getstart/pdf/hyprspec.pdf
- Hejmanowska B. Wężyk P., 2021 (red.) Satellite data for public administration (in Polish), Polska Agencja Kosmiczna, © Copyright by Polska Agencja Kosmiczna 2020
- Lillesand T.M., Kiefer R.W.: Remote Sensing and Image Interpretation. John Wiley & Sons, 2004
Scientific research and publications
Research- 2020-2022 Integration of remote sensing data for control in the agricultural direct payments system (IACS), Excellence Initiative - Research University - AGH
- Glowienka E., Zembol N.: Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images. Geomatics and Environmental Engineering 16(4):103-117, 2022, DOI: 10.7494/geom.2022.16.4.103
- Hejmanowska, B.; Kramarczyk, P.; Głowienka, E.; Mikrut, S. Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images. Remote Sens. 2021, 13, 3176. https://doi.org/10.3390/rs13163176 3.
- Michałowska M., Głowienka E.: Multi-Temporal Analysis of Changes of the Southern Part of the Baltic Sea Coast Using Aerial Remote Sensing Data. 2022, Remote Sensing 14(5), http://dx.doi.org/10.3390/rs14051212