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Information
| Code | UA0028 |
| Name | Remote sensingand Image Interpretation II |
| 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 | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
to teach the formation of digital images and operations with digital images
Course Content
Remote sensing concepts, basics of remote sensing, digital image formation, resolution types, digital image enhancement methods
Course Precondition
None
Resources
remote sensing and image interpretation; Lilesand and Kiefer
Notes
lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | applies mathematical operations to a digital image |
| LO02 | comprehend the formation of digital image |
| LO03 | applies image enhancement operations |
| LO04 | Understands classification techniques |
| LO05 | makes object-based classification |
| LO06 | makes classification based on neural network |
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 | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | The students generate information using remotely sensed data and GIS together with database management skills. | |
| 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 | 5 |
| 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. | |
| 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. | |
| 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 | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | The basic principles of remote sensing | no need any preperation | Öğretim Yöntemleri: Tartışma |
| 2 | basic concepts of remote sensing | no need any preperation | Öğretim Yöntemleri: Anlatım |
| 3 | scanner systems and image formation in remote sensing | topic repetition | Öğretim Yöntemleri: Anlatım |
| 4 | digital image concept and basic mathematical operations | topic repetition | Öğretim Yöntemleri: Anlatım |
| 5 | digital image concept and basic mathematical operations | topic repetition | Öğretim Yöntemleri: Anlatım |
| 6 | image enhancement | topic repetition | Öğretim Yöntemleri: Anlatım |
| 7 | contrast and spatial operations | topic repetition | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | general topic repetition | Ölçme Yöntemleri: Ödev |
| 9 | classification | topic repetition | Öğretim Yöntemleri: Anlatım |
| 10 | classification in digital images | topic repetition | Öğretim Yöntemleri: Anlatım |
| 11 | object-based classification | topic repetition | Öğretim Yöntemleri: Anlatım |
| 12 | neural network | topic repetition | Öğretim Yöntemleri: Anlatım |
| 13 | neural network classification | topic repetition | Öğretim Yöntemleri: Anlatım |
| 14 | change detection | topic repetition | Öğretim Yöntemleri: Anlatım |
| 15 | Image time series analysis | topic repetition | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | general topic repetition | Ölçme Yöntemleri: Ödev |
| 17 | Term Exams | general topic repetition | Ölçme Yöntemleri: Ödev |
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 | ||