Geospatial Data Analysis In Remote Sensing
Geodesy and Geoinformation Engineering
Institute of Natural and Applied Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Geospatial Data Analysis In Remote Sensing | JJM607 | Elective | Doctorate degree | 1 | Fall | 8 |
Name of Lecturer(s)
Prof. Dr. Ozan ARSLAN
Learning Outcomes of the Course Unit
1) Students learn about basic knowledge on data processing systems and methods in remote sensing
2) Students learn about spatial analysis methods, statistical concepts on digital images and mathematical modelling in remote sensing.
3) Students acquire general overview for correction methods on geometrical distortions of image data and polynomial functions
4) Students acquire some knowledge on the relationships about image rectification and spatial statistics and orthorectification
5) Students learn about acquaring basic information for hyperspectral image data analysis, image cube and feature extraction
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||
| 1 | 2 | 3 | 4 | ||
| Learning Outcomes | |||||
| 1 | No relation | No relation | High | No relation | |
| 2 | No relation | No relation | High | No relation | |
| 3 | No relation | No relation | Middle | No relation | |
| 4 | No relation | No relation | High | No relation | |
| 5 | No relation | No relation | High | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Remote Sensing
Course Contents
Introduction, data processing systems, spatial data sources, data formats and GIS, correction of geometrical distortions on image data, polynomial methods for rectification, rational polynomial function model, statistical methods for the selection of control points, spatial analysis methods in remote sensing, mathematical modelling, spatial correlation concept in remote sensing, pattern recognition, spectral transformations, band rationing, principal components, contrast enhancement, Fourier transform, wavelet analysis, image rectification, orthorectification, classification methods, data integration.
Weekly Schedule
1) Data processing methods in remote sensing2) Spatial data sources, data formats and GIS
3) Correction of geometric distortions on image data
4) Geometric distortion correction with rational polynomial functions
5) Statistical methods for selecting control points for geometric distortions
6) Image rectification and spatial statistics, autocorrelation
7) Spatial statistic methods in remote sensing, mathematic models
8) Midterm exam
9) Spatial correlation analysis in remote sensing
10) Pattern recognition, spectral transforms, band rationing
11) Fourier transforms and wavelet analysis
12) Classification algorithms for remotely sensed data
13) Remote sensing and GIS integration
14) Hyperspectral data analysis, image cube and feature extraction
15) Classification for hyperspectral image data
16) Final exam
Recommended or Required Reading
1- Griffth, Daniel A. and Larry J. Layne. A Casebook For Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets, New York: Oxford Press, 1999.
2- Longley, Paul A. and Michael Batty, eds. Advanced Spatial Analysis: The CASA Book of GIS, Redlands, CA: ESRI Press, 2003.
3- Albert, Donald P., William M. Gesler, and Barbara Levergood, eds. Spatial Analysis, GIS, and Remote Sensing Applications in the Health Sciences, Chelsea, MI: Ann Arbor Press, 2000.
4- Melnick, Alan L. Introduction to Geographic Information Systems in Public Health, Gaithersburg, MD: Aspen Publishers, Inc. 2002.
5- Mitchell, Andy. The ESRI Guide to GIS Analysis: Volume 1: Geographic Patterns and Relationships, Redlands, CA: ESRI Press, 1999.
6- Mitchell, Andy. The ESRI Guide to GIS Analysis: Volume 2: Spatial Measurements and Statistics, Redlands, CA: ESRI Press, 2005.
7- Clark, Isobel and William V. Harper. Practical Geostatistics 2000, Columbus, Ohio: Ecosse North America, LLC, 2000.
8- Lillesand,T.M.&Kiefer,R.W., (1999), Remote sensing and image interpretation, 4th ed. ISBN 0471255157, , 736 pp.
9- Danson and Plummers, 1995. Advances in Environmental Remote Sensing, RICS Books.
10- Jensen, J. R., 1996. Introductory Digital Image Processin g: A Remote Sensing Perspective, Prentice Hall Inc., USA.
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Drill and Practice
4) Demonstration
5) Lab / Workshop
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
40% |
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Contribution of Final Examination to Course Grade |
60% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required