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Project Computer Vision for Remote Sensing



#41012 / #1

Seit WS 2020/21

Fakultät IV

EN 5

Institut für Technische Informatik und Mikroelektronik

34342200 FG Remote Sensing Image Analysis

Demir, Begüm

Ravanbakhsh, Sayyed Mahdyar

POS-Nummer PORD-Nummer Modultitel
2349937 42714 Project Computer Vision for Remote Sensing

Learning Outcomes

Participants of this project course gain practical experience in applying computer vision techniques to address Earth observation questions in a collaborative team and acquire knowledge on state-of-the-art topics in the field of computer vision for remote sensing.


Recent advances in satellite technology have led to a regular, frequent, and high-resolution monitoring of Earth at the global scale, providing an unprecedented amount of Earth observation (EO) data. To efficiently process and analyze the large-amount EO data, remote sensing has evolved into a multidisciplinary field, where machine learning and computer vision algorithms play an important role nowadays. At the start of this project course, students receive project topics as well as some information material in the field of computer vision for remote sensing. After setting the project teams and topics, a project environment is decided (with the suitable tools for a team work) with the assistance of the lecturer. Then, project planning, coordination and development start. During the weekly project meetings, each project team presents progress and then further steps are decided in consultation with the lecturer. The project is concluded with final reports as well as a final presentation. The general topics include but are not limited to: i) feature extraction and learning; ii) classification and retrieval of satellite images; iii) change detection and analysis of image time series; iv) super-resolution in the spectral and spatial domain; v) target detection and object recognition; vi) multi-sensor and multi-source data fusion; and vii) estimation of biophysical parameters.

Module Components


All Courses are mandatory.

Course Name Type Number Cycle Language SWS VZ
Project Computer Vision for Remote Sensing PJ WS/SS English 6

Workload and Credit Points

Project Computer Vision for Remote Sensing (PJ):

Workload description Multiplier Hours Total
Attendance 15.0 6.0h 90.0h
Pre/post processing 15.0 12.0h 180.0h
270.0h(~9 LP)
The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

This module contains a guided and self-organized project work. The students get a brief overview of the fundamentals and the recent developments in the area of computer vision for remote sensing. The students work in small teams on a chosen topic, and they present initial findings in an intermediate talk. Each team implements the project and presents insights, methods and results in a concluding talk. Finally, project reports are submitted.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Solid programming skills are required in at least one of the following programming languages: Java, C++, Python. Basic knowledge in machine learning and computer vision is required.

Mandatory requirements for the module test application:

No information

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 points in total



Test elements

Name Points Categorie Duration/Extent
(Deliverable assessment) Intermediate presentation 10 oral approx. 15 minutes
(Deliverable assessment) Final presentation 20 oral approx. 20 minutes
(Deliverable assessment) Technical Documentation 10 written ~5-10 pages
(Deliverable assessment) Scientific Report 20 written ~20-30 pages
(Deliverable assessment) Implementation 40 practical approx 120 hours

Grading scale

Test description (Module completion)

The overall grade for the module consists of the results of the course work ('portfolio exam'). The following are included in the final grade: 1. Intermediate presentation (10p): The students present their initial findings and results on their topic. 2. Final presentation (20p): The students present their final findings/results. 3. Technical Documentation (10p): The students prepare a technical documentation of their codes. 4. Scientific Report (20p): The students summarize their final findings, methods and results in a written scientific report. 5. Implementation (40p): The students work in a team on a selected topic and develop its prototypical implementation.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 15.

Registration Procedures

Students intending to take this project course need to follow the instructions on the RSiM website for pre-semester application. Within the first 6 weeks after the commencement of the project, students will have to register for the module at QISPOS (university examination protocol tool) and, additionally, at ISIS for teaching materials and communication.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

Electronical lecture notes

Availability:  unavailable


Recommended literature
R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 1st Edition, 2010.
T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008

Assigned Degree Programs

This moduleversion is used in the following modulelists:


No information