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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of GEOMATICS ENGINEERING
Masters with Thesis
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FBE
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of GEOMATICS ENGINEERING / Masters with Thesis
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JDZ5820Digital Image Processing in Remote Sensing3+0+0ECTS:7.5
Year / SemesterFall Semester
Level of CourseSecond Cycle
Status Elective
DepartmentDEPARTMENT of GEOMATICS ENGINEERING
Prerequisites and co-requisitesNone
Mode of Delivery
Contact Hours14 weeks - 3 hours of lectures per week
LecturerDoç. Dr. Volkan YILMAZ
Co-Lecturer
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
The objective of this course is to teach the theory and mathematical principles of digital image processing techniques used frequently in projects that use remotely sensed images. Upon successful completion of this course, students also will be able to write codes of digital image processing algorithms using some leading programming languages.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : learn how to apply digital image processing techniques to remotely sensed images 1,2,3,4,5,6,7,8,9,10,11,121,3
PO - 2 : make raw remote sensing data useful by applying required pre-processing algorithms1,2,3,4,5,7,8,9,10,11,121,3
PO - 3 : which digital image processing technique should be used for a particular remote sensing problem1,2,3,4,5,6,7,8,9,10,11,121,3
PO - 4 : write codes on by himself/herself for digital image processing techniques and apply them to solve remote sensing problems 1,2,3,4,5,6,7,8,9,10,11,121,3
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
Image histogram and feature space concepts. Band ratios and vegetation indexes. PCA transform. RGB-IHS transform. Tasseled-Cap transforms. Contrast enhancement techniques. Spatial Transforms. Linear, statistical and gradient filters. Fourier transforms. Wavelet transforms.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Definition of digital image. Process of creating digital images. Conceps of pixel, band, panchromatic, multispectral and hyperspectral images.
 Week 2Concept of resolution. Spatial, spectral, radiometric and temporal resolution concepts.
 Week 3İmage histogram. Definition of contrast enhancement. Linear and non-linear contrast enhancement methods.
 Week 4Image transforms. Arithmetic operations (sum, difference, division and multiplication of two images).
 Week 5Vegitation indexes. NDVI, TNDVI, Tasseled-cap transform.
 Week 6PCA transform. Kontrast enhancement using PCA tranform. İmage fusion using PCA transform
 Week 7Color concept. Theory of color images. RGB and IHS color spaces and their definitions.
 Week 8Mid-term exam
 Week 9Conversion between different color spaces. Color transform between RGB and IHS color spaces. Using IHS color space for contrast enhancement and image fusion.
 Week 10Filtering techniques. Spatia domain low-pass filters. Spatial domain high-pass filters. Spatial domain edge detecters. Frequency domain filters.
 Week 11Introduction to Fourier transform. 1-D Fourier transform.
 Week 122-D Fourier transform. Applications of discrete Fourier transform
 Week 13The Discrete Wavelet Transform. Introduction. The one-dimensional discrete wavelet transform
 Week 14The two-dimensional discrete wavelet transform.
 Week 15Applications of Discrete Wavelet Transform.
 Week 16End-of-term exam
 
Textbook / Material
1Schowengerdt, R.A. 2007; Remote Sensing Models and Methods for Image Processing. Third Edition. Elsevier, USA.
 
Recommended Reading
1Gonzales, R.C., Woods, R.E. 2008; Digital Image Processing. Prentice Hall, USA.
2Mather, P.M., 1987. Computer Processing of Remotely Sensed Images, USA.
3Campbell, J. B., 1996. Introduction to Remote Sensing, The Guilford Press
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 1 30
Homework/Assignment/Term-paper 12 1 20
End-of-term exam 16 1 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 6 8 48
Arasınav için hazırlık 7 6 42
Arasınav 1 1 1
Ödev 4 8 32
Dönem sonu sınavı için hazırlık 6 5 30
Dönem sonu sınavı 1 1 1
Total work load196