Z8114 Remote sensing digital image processing

Faculty of Science
Autumn 2020
Extent and Intensity
2/2. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Kateřina Tajovská, Ph.D. (lecturer)
Mgr. Lukáš Slezák (seminar tutor)
Ing. Kateřina Tajovská, Ph.D. (seminar tutor)
Guaranteed by
Ing. Kateřina Tajovská, Ph.D.
Department of Geography – Earth Sciences Section – Faculty of Science
Contact Person: Ing. Kateřina Tajovská, Ph.D.
Supplier department: Department of Geography – Earth Sciences Section – Faculty of Science
Timetable
Mon 12:00–13:50 Z4,02028
  • Timetable of Seminar Groups:
Z8114/01: Mon 14:00–15:50 Z1,01001b, L. Slezák, K. Tajovská
Z8114/02: Wed 14:00–15:50 Z1,01001b, L. Slezák, K. Tajovská
Prerequisites
Z8108 Remote sensing || PROGRAM(KOS)
Basic knowledge of Remote Sensing
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30
fields of study / plans the course is directly associated with
Course objectives
At the end of the course, students should be able to understand the basic approaches to digital image processing. Moreover, they should be able to use practically selected sw tools for image processing. The main activities are focused on the process of authomatic image classification. Main objectives can be summarized as follows: Hard copy images and digital images. Interpretation of hard copy imagery Principles of visual interpretation, interpretation keys Advantages and drawbags of hard copy imagery and visual interpretation Characteristics of digital imagery. Raster data format and its specific features Remotely sensed data chracter. Basic types of image resolution Image Data storage. Comonn and specific data formats. Image compression. Auxiliary data. Basic steps of digital image data processing Preprocessing, radiometric and atmospheric corrections. Geometric correction and transformation. Image enhancement, image histogram. Principles of automatic classification. Supervised and unsupervised approaches New methods of image classification. Image processing of RADAR and hyperspectral data. Change detection. At the end of the course student should be able to understand basic image procesing (IP) methods explained in individual lectures. He/she would be able to explain when to apply individual IP methods and make reasoned decisions about preconditions that are necessary for proper utilization of IP methods in question. He/she would be able to work with information on satellite imagery preprocessing, make deductions based on acquired knowledge concerning IP methods and properly interpret and validate results of analysis.
Learning outcomes
At the end of this course the student will be able to understand and explain the basic methods of image processing explained in individual lessons.
It will be able to explain when to use individual methods and to provide a rational reasoning on the conditions of using multispectral analysis methods.
He should be able to interpret and verify the results of the image analysis by qualified decisions on satellite data pre-processing, application of methods and, above all, on the basis of acquired knowledge.
Syllabus
  • 1. Basic properties of digital image A-D conversion, DN and its meaning, histogram, multispectral and hyperspectral data, types of vizualization, color spaces, RGB system
  • 2. Preprocessing of digital imagery Radiometric and atmospheric corrections - basic algorithms, geometric correction - overview of common methods(polynomic transformation, splines, TIN, orthocorrection using DEM
  • 3. Image enhancement I. Radiometric (point) enhancements, working with histogram, image contrast, basic types of radiometric enhancements, LUT, density slicing
  • 4. Image enhancement II. Spatial enhancements - image filtering, principles and basic algorithms of high and low pass filtering, Fourier transformation, textural analysis of RADAR images
  • 5. Image enhancements III. Multi band image transformations, color transformations, IHS x RGB, principal component analysis, ratio images, spectral (vegetation) indicies, TASSELED CAP
  • 6. Supervised image classification I. Spectral signatures and feature space, common approaches, training sites
  • 7. Supervised image classification II. Per-pixel classificators - parelellepipeds, minimum distance, maximum likelyhood, spectral signatures and its statistical description and evaluation. Evaluation of image classification - error matrix, test sites.
  • 8. Unsupervised image classification Spectral and information classes, cluster analysis, ISODATA a K-MEANS, aggregation, postclassification corrections
  • 9. New approaches to image classification Fuzzy classification, neural networks, textural and contextual classification, SAM, ECHO
  • 10. RADAR data image processing. Specifc features of RADAR imagery, basic algorithms, filtering, textural analysis, examples using RADAR imagery
  • 11. Principles of hyperspectral image analysis. Hyperspectral cube, "mixels" and "pure" pixels, spectral libraries, endmembers, hyperspectral data classification - unmixing
  • 12. Basic approaches to change detection Ratio images, classification comparison, Change vector analysis, PCA
Literature
    required literature
  • DOBROVOLNÝ, Petr. Dálkový průzkum Země. Digitální zpracování obrazu. 1. vyd. Brno: Masarykova univerzita, 1998, 208 s. ISBN 8021018127. info
  • LILLESAND, Thomas M., Ralph W. KIEFER and Jonathan W. CHIPMAN. Remote sensing and image interpretation. 6th ed. New York: John Wiley & Sons, 2008, xii, 756. ISBN 9780470052457. info
  • CAMPBELL, James B. and Randolph H. WYNNE. Introduction to remote sensing. Fifth edition. London: Guilford Press, 2011, xxxi, 667. ISBN 9781609181765. info
    not specified
  • Computer processing of remotely sensed imagesan introduction. Edited by Paul M. Mather. 4th ed. Chichester, West Sussex, England: John Wiley & Sons, 2011, xx, 434 p. ISBN 9780470742396. info
  • Remote sensing, models, and methods for image processing. Edited by Robert A. Schowengerdt. 3rd ed. Burlington, MA: Academic Press, 2007, 515 p. ISBN 0123694078. info
  • Urban remote sensing. Edited by Qihao Weng - Dale A. Quattrochi. Boca Raton, Fla.: CRC Press, 2007, 412 s. ISBN 9780849391996. info
  • HALOUNOVÁ, Lena and Karel PAVELKA. Dálkový průzkum Země. Vyd. 1. Praha: Vydavatelství ČVUT, 2005, 192 s. ISBN 8001031241. info
  • LIANG, Shunlin. Quantitative remote sensing of land surfaces. Hoboken, N.J.: John Wiley & Sons, 2004, xxvi, 534. ISBN 0471281662. info
  • Environmental modelling with GIS and remote sensing. Edited by Andrew Skidmore. 1st publ. London: Taylor & Francis, 2002, xvi, 268. ISBN 0415241707. info
Teaching methods
Lectures explaining basic terms of digital image processing and presenting individual examples step by step. Practical training based on exercises that are solved using image processing software. Satellite imagery used within the practical courses. Online lessons: https://meet.google.com/btf-itrq-rgn Exercise in person
Assessment methods
The exam has the form of a written test on theory of image processing. Elaboration of all practical excercises and successul practical test at the end of the term are two necessary conditions for passing the exam. Practical test with the use of computer.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
The course ends with an exam in which the student demonstrates the ability to apply digital image processing methods in solving typical geographical problems, the ability to meaningfully use digital image data in GIS.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2004, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2020, recent)
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