UA0019 Multispectral Sensors and Data Processing

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code UA0019
Name Multispectral Sensors and Data Processing
Term 2024-2025 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator


Course Goal / Objective

The aim of this course is to determine how information is obtained from satellite images and how to interpret them, to inform about image analysis and to show how to integrate remote sensing data and GIS data.

Course Content

Detector types and features, Image formation in multispectral sensors, Line-Wishkbroom-Pushbroom detection, Processing of multispectral sensor data and obtaining products

Course Precondition

None

Resources

Chipman, J.W., 2004. Remote Sensing and Image Interpretation, John Wiley & Sons Pres. New York.

Notes

Lecture notes/presentations prepared by the instructor of the course


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Learns geometric and spectral properties of remotely sensed, analog and digital images and information about optical and microwave systems.
LO02 Learns knowledgeable about data received from satellites located in different orbits
LO03 Detects satellite imagery features and makes it suitable for remote sensing and GIS users
LO04 Designs, manages and presents a simple remote sensing project


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal At the end of the programme, the students acquire advanced knowledge on remote sensing and GIS theory.
PLO02 Bilgi - Kuramsal, Olgusal The students gain knowledge on remote sensing technologies, sensors and platforms and remotely sensed data. 5
PLO03 Bilgi - Kuramsal, Olgusal The students generate information using remotely sensed data and GIS together with database management skills. 4
PLO04 Bilgi - Kuramsal, Olgusal The students develop the necessary skills for selecting and using appropriate techniques and tools for engineering practices, using information technologies effectively, and collecting, analysing and interpreting data. 3
PLO05 Bilgi - Kuramsal, Olgusal The students gain knowledge to use current data and methods for multi-disciplinary research.
PLO06 Bilgi - Kuramsal, Olgusal The students gain technical competence and skills in using recent GIS and remote sensing software.
PLO07 Bilgi - Kuramsal, Olgusal The students acquire knowledge on potential practical fields of use of remotely sensed data, and use their theoretical and practical knowledge for problem solution in the related professional disciplines. 4
PLO08 Yetkinlikler - Öğrenme Yetkinliği Students will be able to calculate and interpret physical and atmospheric variables by processing the satellite data. 5
PLO09 Yetkinlikler - Öğrenme Yetkinliği Students can generate data for GIS projects using Remote Sensing techniques. 4
PLO10 Bilgi - Kuramsal, Olgusal Gains the ability to analyze and interpret geographic data with GIS techniques.
PLO11 Bilgi - Kuramsal, Olgusal Gains the ability of problem solving, solving, solution oriented application development. 2
PLO12 Yetkinlikler - Öğrenme Yetkinliği Acquires the ability to acquire, evaluate, record and apply information from satellite data. 5


Week Plan

Week Topic Preparation Methods
1 Introduction to hyperspectral image (HSG) processing Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
2 Standard processing steps Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
3 Current challenges Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
4 Extraction of physical properties from hyperspectral image (HSG) Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama
5 Extraction of spatial features from hyperspectral image (HSG) Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama
6 Extraction of advanced spatial/spectral features from a hyperspectral image (HSG) Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama
7 Introduction to Trained classification in hyperspectral image (HSG) Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
8 Mid-Term Exam Preparing for the exam and rewieving of the topics Ölçme Yöntemleri:
Ödev, Proje / Tasarım
9 Preliminary information in hyperspectral image (HSG) Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
10 Content information in hyperspectral image (HSG) Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
11 Multi-source image compositing in hyperspectral image (HSG): SAR, LiDAR and ancillary data Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama
12 Definitions in hyperspectral image (HSG): Mixture models Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
13 Identification and extraction of the last member in the hyperspectral image (HSG) Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama
14 Advanced techniques in hyperspectral imaging (HSG): sparse, contextual and nonlinear models Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
15 Extraction of biophysical parameters Related subjects in the course text book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama
16 Term Exams Preparing for the exam and rewieving of the topics Ölçme Yöntemleri:
Ödev, Proje / Tasarım
17 Term Exams Preparing for the exam and rewieving of the topics Ölçme Yöntemleri:
Ödev, Proje / Tasarım


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 23.09.2024 03:05