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Information
| Code | PM558 |
| Name | Land Classification Techniques in Remote Sensing |
| Term | 2023-2024 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 | Prof. Dr. SÜHA BERBEROĞLU |
| Course Instructor |
1 |
Course Goal / Objective
The methods, software and algorithms used in Geographic Information Systems (GIS) land classification techniques will be introduced. Image processing techniques, GIS environment and terminology are among the other topics of the course.
Course Content
Definition of land use and cover, importance and uses of this information, remote sensing and land classification and its advantages, classification techniques used, algorithms and the basic elements of remote sensing (reflection, spatial resolution and time) are among the subjects of optimum use of land classification.
Course Precondition
There is no precondition
Resources
Lecture Notes
Notes
All publications on the subject
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | To be able to develop and deepen their knowledge at the level of expertise in the same or a different field based on their master level qualifications. |
| LO02 | To be able to comprehend the interdisciplinary interaction related to the field. |
| LO03 | It has an advanced level of knowledge and insight that provides the foundation for original studies in the field of landscape planning and landscape design on the basis of the competencies gained at the undergraduate level. |
| LO04 | Critical awareness of the problems of the nature, sources, knowledge production and testing of information in the interface between landscape planning and other fields related to the field of landscape design. |
| LO05 | To be able to use the theoretical and practical knowledge in the field of expertise. |
| LO06 | To be able to create new information by integrating the information in the field with the information from different disciplines; analyze the problems that require expertise by using scientific research methods. |
| LO07 | Gains the necessary cognitive and practical skills for the professional practice in graduate education. |
| LO08 | Apply gained knowledge, comprehension and problem solving skills in new and non-conventional environments, in a wider, interdisciplinary, multidisciplinary and disciplined context. |
| LO09 | To be able to construct a problem, to develop a solution method, to be able to solve the problem. |
| LO10 | To be able to develop new strategic approaches for solving problems and to be able to produce solutions by taking responsibility. |
| LO11 | To be able to critically evaluate the information related to the field, to direct learning and to carry out advanced studies independently. |
| LO12 | To be able to transfer the current developments and their studies to the groups in the field and outside the field in written, oral and visual form. |
| LO13 | To be able to examine social relations and the norms that direct these relations from a critical point of view, to develop them and to change them when necessary. |
| LO14 | To be able to use information and communication technologies at an advanced level with computer software. |
| LO15 | To be able to develop strategy, policy and implementation plans related to landscape planning and landscape design and to evaluate the obtained results within the framework of quality processes. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Based on Master's level qualifications, they develop their knowledge in the same or a different field at the level of expertise. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Uses the knowledge of the principles, processes and tools of Landscape Architecture together with solutions in the professional field. | |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | The ability to work effectively individually or in multi-disciplinary teams gains the self-confidence to take responsibility. | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Gains the ability to collect data, create methods, analyze and interpret results related to the field. | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | It follows the developments in science and technology and gains the ability to constantly renew itself. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | He/she conveys his/her studies produced in the graduate program in written, oral and/or visual formats in national and international platforms. | |
| PLO07 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | He/she independently carries out a study that requires expertise in his/her field. | |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Supervises the information obtained during the collection, interpretation, implementation and announcement of the data related to the field by considering social, scientific, cultural and ethical values. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | It uses the knowledge and competence to reflect the philosophy, elements, principles and tools of landscape planning and design into the detailed landscape planning and design process. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | It adopts lifelong learning as a principle in the field of Landscape Architecture. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses advanced computer software, information and communication technologies at the level required by the field. | 4 |
| PLO12 | Yetkinlikler - Alana Özgü Yetkinlik | Gains the competence to develop plans and design proposals sensitive to society, area and nature for different landscape types. | |
| PLO13 | Yetkinlikler - Alana Özgü Yetkinlik | To be able to develop strategy, policy and implementation plans on issues related to his/her field and evaluate the results obtained within the framework of quality processes. | |
| PLO14 | Yetkinlikler - Alana Özgü Yetkinlik | Gains and applies the ability to identify, define, formulate and solve problems with related to the field. | |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | It adopts the principle of complying with scientific and ethical values in all its works. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Remotely sensed data and characteristics | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım |
| 2 | Spatial, radiometric, temporal and spectral characteristics | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Image pre-processing and transformation | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Introduction to supervised classification | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Classification using maximum likelihood, Minimum distance, parallel pipe methods | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Introduction to non-parametric classification | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 7 | Introduction to artificial neural networks | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 8 | Mid-Term Exam | Preparation for exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Classification with support vector machines | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 10 | Classification with decision tree algorithms | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Classification accuracy | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 12 | Unsupervised classification methods | Lecture notes, literature readings | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Pre-processing applications before classification | Lecture notes for laboratory practices | Öğretim Yöntemleri: Anlatım, Tartışma |
| 14 | Classification exercise | Lecture notes for laboratory practices | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | Classification exercise and evaluation | Lecture notes for laboratory practices | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 16 | Term Exams | Preparation for exam | Ölçme Yöntemleri: Ödev |
| 17 | Term Exams and evaluation | Preparation for exam | 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 | ||