PM558 Land Classification Techniques in Remote Sensing

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code PM558
Name Land Classification Techniques in Remote Sensing
Term 2024-2025 Academic Year
Semester . Semester
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 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

Update Time: 14.05.2024 05:15