TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (PhD) | |
| Code | UA605 |
| Name | Classification Methods in Remote Sensing-I |
| Term | 2025-2026 Academic Year |
| Term | Fall |
| 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. TOLGA ÇAN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
Basic image classification techniques will be applied on remote sensing data. It is aimed to provide knowledge and skills on basic concepts and applications such as image preprocessing, training data generation, classical supervised classification methods and accuracy assessment using ArcGIS Pro and Image Analyst tools.
Course Content
Using the Image Analyst module of ArcGIS Pro software, topics such as basic processing techniques of satellite and aerial images, training data generation, application of supervised classification algorithms, and accuracy analysis of classification results will be covered theoretically and practically. Students will learn each stage of the classification process comprehensively from data preparation to result interpretation; they will understand the relationship between geographic information systems and image analysis, and they will be able to analyze how to select different classification methods according to data type and purpose. At the end of the course, it is aimed that students will have the competence to apply basic classification techniques, evaluate results, and conduct small-scale classification projects.
Course Precondition
There is no pre requires.
Resources
Imagery and Remote sensing resouces https://www.esri.com/en-us/capabilities/imagery-remote-sensing/resources Fundamentals of Remote Sensing. A Canada Centre for Remote Sensing Remote Sensing Tutorial https://natural-resources.canada.ca/sites/nrcan/files/earthsciences/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf Date modified: 2025-01-08
Notes
Imagery and Remote sensing resouces https://www.esri.com/en-us/capabilities/imagery-remote-sensing/resources Fundamentals of Remote Sensing. A Canada Centre for Remote Sensing Remote Sensing Tutorial https://natural-resources.canada.ca/sites/nrcan/files/earthsciences/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf Date modified: 2025-01-08
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the basic concepts of classification. |
| LO02 | Compare supervised and unsupervised classification methods. |
| LO03 | Apply basic classification algorithms. |
| LO04 | Perform accuracy assessments. |
| LO05 | Evaluate the advantages and disadvantages of different classification methods. |
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. | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | The students generate information using remotely sensed data and GIS together with database management skills. | 3 |
| 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. | |
| 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. | 3 |
| PLO08 | Yetkinlikler - Öğrenme Yetkinliği | Students will be able to calculate and interpret physical and atmospheric variables by processing the satellite data. | 3 |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Students can generate data for GIS projects using Remote Sensing techniques. | 2 |
| 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. | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Acquires the ability to acquire, evaluate, record and apply information from satellite data. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Fundamentals of classification in remote sensing | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 2 | Concept of supervised classification | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 3 | Concept of unsupervised classification | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 4 | Decision boundaries and parameter estimation | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 5 | Minimum distance classification | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 6 | Maximum likelihood classification | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 7 | K-nearest neighbor algorithm | No advance preparation is necessary | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Advance preparation is necessary. | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım |
| 9 | Support vector machines | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 10 | Accuracy assessment and error matrix | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 11 | Feature selection and dimensionality reduction | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 12 | Post-classification image processing techniques | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 13 | Hybrid classification methods | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım |
| 14 | Practical classification exercises | No advance preparation is necessary. | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 15 | Project presentations and evaluation | No advance preparation is necessary. | Öğretim Yöntemleri: Anlatım, Tartışma |
| 16 | Term Exams | Advance preparation is necessary. | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım, Ödev |
| 17 | Term Exams | Advance preparation is necessary. | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım |
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 | ||