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Information
| Code | UA0019 |
| Name | Multispectral Sensors and Data Processing |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| 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 Instructor |
1 |
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 | ||