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#41012 / #1

WiSe 2020/21 - WiSe 2022/23

English

Project Computer Vision for Remote Sensing

9

Demir, Begüm

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34342200 FG Remote Sensing Image Analysis

Keine Angabe

Kontakt


EN 5

Witte, Bethany Jane

b.witte@tu-berlin.de

https://rsim.berlin/

Lernergebnisse

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.

Lehrinhalte

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.

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Project Computer Vision for Remote SensingPJWiSe/SoSeen6
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Project Computer Vision for Remote Sensing (PJ):

AufwandbeschreibungMultiplikatorStundenGesamt
Attendance15.06.0h90.0h
Pre/post processing15.012.0h180.0h
270.0h(~9 LP)
Der Aufwand des Moduls summiert sich zu 270.0 Stunden. Damit umfasst das Modul 9 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

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.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

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.

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Portfolio examination

Art der Portfolioprüfung

100 Punkte insgesamt

Sprache(n)

English

Prüfungselemente

NamePunkteKategorieDauer/Umfang
(Deliverable assessment) Intermediate presentation10mündlichapprox. 15 minutes
(Deliverable assessment) Final presentation20mündlichapprox. 20 minutes
(Deliverable assessment) Technical Documentation10schriftlich~5-10 pages
(Deliverable assessment) Scientific Report20schriftlich~20-30 pages
(Deliverable assessment) Implementation40praktischapprox 120 hours

Notenschlüssel

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

Prüfungsbeschreibung (Abschluss des Moduls)

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.

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
1 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Winter- und Sommersemester.

Maximale teilnehmende Personen

Die maximale Teilnehmerzahl beträgt 15.

Anmeldeformalitäten

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.

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  nicht verfügbar

 

Literatur

Empfohlene Literatur
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

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Dieses Modul findet in keinem Studiengang Verwendung.

Sonstiges

Keine Angabe