Z8114 Remote sensing digital image processing

Faculty of Science
Autumn 2010 - only for the accreditation
Extent and Intensity
2/2/0. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Petr Dobrovolný, CSc. (seminar tutor)
Mgr. František Kuda, Ph.D. (seminar tutor)
Mgr. Andrea Kýnová (seminar tutor)
Guaranteed by
prof. RNDr. Rudolf Brázdil, DrSc.
Department of Geography - Earth Sciences Section - Faculty of Science
Contact Person: prof. RNDr. Petr Dobrovolný, CSc.
Prerequisites (in Czech)
Z8108 Remote sensing || PROGRAM ( N - GK )
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 40 student(s).
Current registration and enrolment status: enrolled: 0/40, only registered: 0/40
fields of study / plans the course is directly associated with
there are 8 fields of study the course is directly associated with, display
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 practicaly selected sw tools for image processing. THe main activities are focused on the proces of authomatic image classification. Main objectives can be summarized as ollows: 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, A- D conversion. 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. Suppervised and unsuppervised 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.
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. Suppervised image classification I. Spectral signatures and feature space, common approaches, training sites, 7. Suppervised 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. Unsuppervised image classification Spectral and information classes, cluster analysis, ISODATA a K-MEANS, aggregation, postclassification corections 9. New approaches to image classification Fuzzy classification, neural networks, textural and contextual classification kontextuální, 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 a "pure" pixels, spectral libraries, endmembers, hyperspectral data classification - unmixing 12. Basic approaches to change detection Ratio images, classification comparison, Change vector analysis, PCA
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. 5th ed. Hoboken, N.J.: John Wiley & Sons, 2004. xiv, 763. ISBN 0471152277. info
  • CAMPBELL, James B. Introduction to remote sensing. New York: Guilford Press, 1987. xxiv, 551. ISBN 0-89862-776-1. info
  • Urban remote sensing. Edited by Qihao Weng - Dale A. Quattrochi. Boca Raton, Fla.: CRC Press, 2007. 412 p. ISBN 9780849391996. info
  • LIANG, Shunlin. Quantitative remote sensing of land surfaces. Hoboken, N.J.: John Wiley & Sons, 2004. xxvi, 534. ISBN 0471281662. info
  • LANDGREBE, David A. Signal theory methods in multispectral remote sensing. Hoboken, New Jersey: John Wiley & Sons, 2003. xi, 508. ISBN 047142028X. info
  • Environmental modelling with GIS and remote sensing. Edited by Andrew Skidmore. 1st publ. London: Taylor & Francis, 2002. xvi, 268. ISBN 0415241707. info
  • KONECNY, Gottfried. Geoinformation : remote sensing, photogrammetry and geographic information systems. 1st publ. London: Taylor & Francis, 2002. xiv, 248. ISBN 0415237955. info
  • Remote sensing change detection :environmental monitoring methods and applications. Edited by Ross S. Lunetta - Christopher D. Elvidge. London: Taylor & Francis, 1999. xviii, 318. ISBN 0-7484-0861-4. info
Teaching methods
Lectures explaining basic terms of digital image processing and presenting individual examples step by step. Practical training based on 11 exercises that are solved using image processing software. Satellite imagery used within the practical courses.
Assessment methods
An exam has the form of written test on theory of image processing. Elaboration of all practical excercises and successul pass the practical test at the end of the term are two necessary conditons to pass the exam. Practical test with the use of computer.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of 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 2020.