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Image Processing for Remote Sensing

6

English

#40937 / #1

Seit SS 2019

Fakultät IV

EN 5

Institut für Technische Informatik und Mikroelektronik

34342200 Remote Sensing Image Analysis

Demir, Begüm

No information

demir@tu-berlin.de

POS-Nummer PORD-Nummer Modultitel
2348573 40690 Image Processing for Remote Sensing

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. The final part of the course will introduce Google Earth Engine, a cloud-based remote sensing and image analysis platform. Students will explore basic image processing techniques with Google Earth Engine. Practical applications will be provided throughout the course.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name Type Number Cycle Language SWS
Image Processing for Remote Sensing IV 3434 L 191 SS English 4

Workload and Credit Points

Image Processing for Remote Sensing (IV):

Workload description Multiplier Hours Total
Attendance 15.0 4.0h 60.0h
Pre/post processing 15.0 8.0h 120.0h
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:

No information

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 points in total

Language

English

Test elements

Name Points Categorie Duration/Extent
(Deliverable assessment) Assessment of homework 50 written 4 x 2h
(Examination) Written test 50 written 75 min

Grading scale

1.01.31.72.02.32.73.03.33.74.0
95.090.085.080.075.070.065.060.055.050.0

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

This module can be completed in one semester.

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):

This moduleversion is used in the following modulelists:

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

This lecture is of interest in the following study areas: - Information systems - Digital media and human copmuter interaction - Cognitive Systems

Miscellaneous

Computer Engineering (Master of Science) Computer Science (Informatik) (Master of Science) Double-Degree-Masterstudiengang ICT Innovation (Master of Science) Elektrotechnik (Master of Science) Wirtschaftsinformatik/Information Systems Management (Matser of Science) Wirtschaftsingeniuerwesen (Matser of Science)