TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Doctorate Of Science
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know the pre-processing and enhancement techniques of satellite imagery. 2) Know the spectral classification in space borne imagery. 3) Know the selection and evaluation of training sites used in classification. 4) Know the supervised and unsupervised classification techniques. 5) Know the digital change detection.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	Image preprocessing and enhancement of satellite imagery, Spectral classification in spaceborne imagery, Selection and evaluation of training sites, Different methods of unsupervised (sequential cluster, statistical cluster, isodata) and supervised (parallelepiped, minimum distance, Mahalanobis distance, maximum likelihood/ Bayesian) classification, Digital change detection.
COURSE CONTENTS	WEEK TOPICS 1st Week Image preprocessing and enhancement of satellite imagery. 2nd Week Image preprocessing and enhancement of satellite imagery. 3rd Week Spectral classification in spaceborne imagery. 4th Week Spectral classification in spaceborne imagery. 5th Week Selection and evaluation of training sites. 6th Week Selection and evaluation of training sites. 7th Week Selection and evaluation of training sites. 8th Week Mid-term 9th Week Different methods of unsupervised (sequential cluster, statistical cluster, isodata) and supervised (parallelepiped,minimum distance 10th Week Different methods of unsupervised (sequential cluster, statistical cluster, isodata) and supervised (parallelepiped,minimum distance 11th Week Mahalanobis distance, maximum likelihood/ Bayesian) classification. 12th Week Mahalanobis distance, maximum likelihood/ Bayesian) classification. 13th Week Digital change detection. 14th Week Digital change detection.
RECOMENDED OR REQUIRED READING	1. Jensen, J., "Introductory Digital Image Processing", 3rd edition, Prentice Hall, (2004). 2. Richards, J.A., & Jia, Xiuping, J., "Remote Sensing Digital Image Analysis: An Introduction", 4th edition, Springer (2005). 3. Lillesand, T., Kiefer, R.W., & Chipman, J., "Remote Sensing and Image Interpretation", 6th edition, Wiley, (2007).
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Problem Solving,Practice,Project,Report Preparation,Presentation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 15 Project 1 15 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 11 154 Preparation for Final exam 1 69 69 Course hours 14 3 42 Preparation for Midterm exam 1 44 44 Laboratory (including preparation) Final exam 1 2 2 Homework Total Workload 313 Total Workload / 30 10,43 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No