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#40937 / #2

SoSe 2022 - WiSe 2022/23

English

Image Processing for Remote Sensing

6

Demir, Begüm

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34342200 FG Remote Sensing Image Analysis

No information

Kontakt


EN 5

Demir, Begüm

demir@tu-berlin.de

Learning Outcomes

Participants of this course will gain theoretical and practical knowledge on fundamental concepts and techniques for processing and analysis of remote sensing images acquired by Earth observation satellite and airborne systems.

Content

This course will introduce fundamental concepts and techniques in the content of remote sensing and image processing for Earth observation from space. The course starts by introducing core concepts in remote sensing (describing the processes by which images are captured by sensors mounted on satellite and airborne platforms and key characteristics of the acquired images). Then, fundamental methodologies for processing, analyzing, and visualizing remotely sensed imagery are introduced. Topics include representation of high-dimensional remote sensing images, time and frequency domain representations, filtering and enhancement. Practical applications will be provided throughout the course.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWS ISIS VVZ
Image Processing for Remote SensingIV3434 L 191SoSeEnglish4

Workload and Credit Points

Image Processing for Remote Sensing (IV):

Workload descriptionMultiplierHoursTotal
Attendance15.04.0h60.0h
Pre/post processing15.08.0h120.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The module consists of conventional frontal teaching in class and computer laboratory exercises designed to practically rehearse the theory taught in the lectures. The lab exercises introduce various remote sensing image processing topics, which will be examined in more detail in the homework assignments.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Good knowledge in Mathematics, especially linear algebra and statistics. Basic programming knowledge.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

NamePointsCategorieDuration/Extent
(Deliverable assessment) Assessment of homework50written4 x 2h
(Examination) Written exam50written60 min

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt95.0pt90.0pt85.0pt80.0pt75.0pt70.0pt65.0pt60.0pt55.0pt50.0pt

Test description (Module completion)

The grade for this module consists of two parts (each weighted with 50%): 1. Assessment of home exercises 2. Written test

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:
Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 50.

Registration Procedures

Registration at QISPOS (universitiy examination protocol tool) within the first 6 weeks of the semester and registration on ISIS for teaching materials and communication.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  available

 

Literature

Recommended literature
1) G. Camps-Valls, D. Tuia, L. Gómez-Chova, S. Jiménez and J. Malo, Remote Sensing Image Processing, Editors Morgan & Claypool Publishers 2011
2) T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008
3) R. C. Gonzalez., and R. E. Woods, Digital Image Processing, 2nd edition, Prentice Hall, 2001.

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information