TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (PhD) | |
| Code | UA602 |
| Name | Advanced Remote Sensing Technologies |
| Term | 2025-2026 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 | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. NAZIM AKSAKER |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to introduce advanced technologies in remote sensing, and to provide theoretical and practical knowledge on hyperspectral, radar, and LiDAR data processing techniques. The course aims to enhance students' abilities in analyzing and interpreting remote sensing data.
Course Content
Hyperspectral remote sensing, radar remote sensing (Sentinel-1, SAR), LiDAR technology, advanced image processing techniques, object-based classification, data fusion, 3D modeling, big data processing, machine learning applications, artificial intelligence in remote sensing.
Course Precondition
There is no pre requires
Resources
Richards, J. A. (2013). Remote Sensing Digital Image Analysis. Jensen, J. R. (2015). Introductory Digital Image Processing. Schowengerdt, R. A. (2007). Remote Sensing: Models and Methods for Image Processing. Güncel makaleler ve ders notları / Recent articles and lecture notes.
Notes
Richards, J. A. (2013). Remote Sensing Digital Image Analysis. Jensen, J. R. (2015). Introductory Digital Image Processing. Schowengerdt, R. A. (2007). Remote Sensing: Models and Methods for Image Processing. Güncel makaleler ve ders notları / Recent articles and lecture notes.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Identify advanced remote sensing technologies |
| LO02 | Process hyperspectral, radar, and LiDAR data. |
| LO03 | Apply object-based classification and data fusion. |
| LO04 | Use artificial intelligence in remote sensing applications. |
| LO05 | Explain big data processing techniques. |
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. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | The students gain knowledge on remote sensing technologies, sensors and platforms and remotely sensed data. | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | The students generate information using remotely sensed data and GIS together with database management skills. | 2 |
| 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. | |
| PLO05 | Bilgi - Kuramsal, Olgusal | The students gain knowledge to use current data and methods for multi-disciplinary research. | 2 |
| PLO06 | Bilgi - Kuramsal, Olgusal | The students gain technical competence and skills in using recent GIS and remote sensing software. | 3 |
| 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. | |
| PLO08 | Yetkinlikler - Öğrenme Yetkinliği | Students will be able to calculate and interpret physical and atmospheric variables by processing the satellite data. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Students can generate data for GIS projects using Remote Sensing techniques. | 3 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Gains the ability to analyze and interpret geographic data with GIS techniques. | 2 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Gains the ability of problem solving, solving, solution oriented application development. | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Acquires the ability to acquire, evaluate, record and apply information from satellite data. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Emerging trends in remote sensing | There is no preliminary preparation | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Introduction to hyperspectral remote sensing | There is no preliminary preparation | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Hyperspectral data processing techniques | There is no preliminary preparation | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Introduction to radar remote sensing | There is no preliminary preparation | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | SAR data processing and interpretation | There is no preliminary preparation | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Anlatım |
| 6 | Introduction to LiDAR technology | There is no preliminary preparation | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | LiDAR data processing and applications | There is no preliminary preparation | Öğretim Yöntemleri: Anlatım, Tartışma |
| 8 | Mid-Term Exam | There is preliminary preparation | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım |
| 9 | Object-based image analysis | There is no preliminary preparation | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 10 | Data fusion techniques | There are no prerequisites | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | 3D modeling and applications | There are no prerequisites | Öğretim Yöntemleri: Tartışma, Anlatım |
| 12 | Big data processing approaches | There are no prerequisites | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 13 | Machine learning in remote sensing | There are no prerequisites | Öğretim Yöntemleri: Tartışma, Anlatım |
| 14 | AI applications in remote sensing | There are no prerequisites | Öğretim Yöntemleri: Anlatım, Tartışma |
| 15 | Project presentations and general evaluation | There are no prerequisites | Öğretim Yöntemleri: Anlatım, Tartışma |
| 16 | Term Exams | There are prerequisites | Ölçme Yöntemleri: Proje / Tasarım, Ödev, Yazılı Sınav |
| 17 | Term Exams | There are prerequisites | Ölçme Yöntemleri: Proje / Tasarım, Ödev, Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 15 | 3 | 45 |
| Out of Class Study (Preliminary Work, Practice) | 15 | 4 | 60 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 20 | 20 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 10 | 10 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||