Moses - Machine Learning for Remote Sensing Data Analysis

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SS 2019 - SoSe 2023. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#40928 / #2

Validity:
SS 2019 - SoSe 2023

Default display language:
English

Module title:
Machine Learning for Remote Sensing Data Analysis

Credits:
3

Responsible person:
Demir, Begüm

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Technische Informatik und Mikroelektronik

Area of expertise:
No information

Prüfungsausschuss:
No information

Kontakt

Office:
EN 5

Contact person:
Fuchs, Martin Hermann Paul

E-mail address:
m.fuchs@tu-berlin.de

Website:

http://rsim.berlin

Learning Outcomes

Participants of this seminar will acquire knowledge on advanced methodologies for the analysis of remote sensing images acquired by the last generation Earth observation satellite systems. In particular, this seminar course will provide students on the one hand an in-depth theoretical and practical knowledge on remote sensing image analysis; and on the other hand a know-how in one or more domains of applications, such as land-cover maps generation, land-cover maps updating, biophysical parameter estimation, image search and retrieval, change detection. Moreover, the students will learn about the current developments in remote sensing and related data analysis methods, and how machine learning techniques can be employed to solve Earth observation questions.

Content

Remote sensing images are a rich information source for monitoring the Earth surface, e.g., for climate change analysis, urban area studies, forestry applications, risk and damage assessment, water quality assessment, crop monitoring. Due to the recent developments in both passive multispectral and hyperspectral sensors, and synthetic aperture radar active instruments, the role of this technology is becoming more and more important for understanding the dynamics of our planet. To efficiently process/analyze the data, remote sensing has evolved into a multidisciplinary field, where machine learning algorithms play an important role nowadays. In this seminar, students will review the current state of the art in the field of machine learning applied to remote sensing image analysis in the framework of different Earth observation applications. The general topics include but are not limited to: i) feature selection and extraction; ii) supervised, unsupervised, semi-supervised classification and regression, iii) active learning, iv) structured learning, v) transfer learning and domain adaptation with applications to remote sensing image analysis.

Module Components

RSiM:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Machine Learning for Remote Sensing Data Analysis	SEM		WiSe	English	2

Workload and Credit Points

Machine Learning for Remote Sensing Data Analysis (SEM):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Pre/post processing	15.0	4.0h	60.0h
			90.0h(~3 LP)

The Workload of the module sums up to 90.0 Hours. Therefore the module contains 3 Credits.

Description of Teaching and Learning Methods

At the beginning of the semester there will be overview lectures that will provide some background concepts on remote sensing, different types of remote sensing images and the general remote sensing image processing/analysis chain. In addition, a set of primary literature works will be provided. Then, the students will further investigate the topics assigned to them in the seminar. A few weeks after they will give a short presentation of approx. 10 minutes (based on their initial understandings on the considered topic) that will be open to all seminar participants. In the further course of the semester the students will give a final presentation of approx. 30 minutes. In addition to the talk the students will also prepare a technical report with 10-15 pages describing their topic.

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

Name	Points	Categorie	Duration/Extent
Long Seminar Presentation	45	oral	30 minutes
Short Seminar Presentation	15	oral	10 minutes
Written Seminar Report	40	written	10-15 pages

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	95.0pt	90.0pt	85.0pt	80.0pt	75.0pt	70.0pt	65.0pt	60.0pt	55.0pt	50.0pt

Test description (Module completion)

There are two oral portfolio deliverable assessments: a short seminar presentation (15 pts.), a long seminar presentation (45 pts.) There is one written portfolio deliverable assessment: a written seminar report (40 pts.)

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:
Wintersemester.

Maximum Number of Participants

The maximum capacity of students is 16.

Registration Procedures

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

Recommended literature
G. Camps-Valls, and L. Bruzzone, Kernel methods for Remote Sensing Data Analysis, Wiley & Sons 2009
L. Bruzzone, B. Demir, A Review of Modern Approaches to Classification of Remote Sensing Data, in Land Use and Land Cover Mapping Europe, Practices and Trends, Eds: I. Manakos, M. Braun, EARSeL Book Series, Springer Verlag, Chapter 9, 2014, pp. 127-143.
E. Alpaydin, Introduction to Machine Learning, MIT Press, Cambridge, Massachusetts, London, England, 2004.

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

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

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte 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

Machine Learning for Remote Sensing Data Analysis