548-0146/01 – Advanced methods in Remote Sensing (PMDPZ)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantorprof. Ing. Jiří Horák, Dr.Subject version guarantorprof. Ing. Jiří Horák, Dr.
Study levelundergraduate or graduateRequirementCompulsory
Study languageCzech
Year of introduction2021/2022Year of cancellation
Intended for the facultiesHGFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
HOR10 prof. Ing. Jiří Horák, Dr.
HOR0401 Ing. Ivana Horáková
JUR02 Ing. Lucie Orlíková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 8+8

Subject aims expressed by acquired skills and competences

The objective is to learn student how to utilize digital image remotely sensed data, how to pre-process data and modify image data to enhance required information, using basic as well as advanced methods of classification of digital images, including object-oriented classification and deep learning methods, processing radar and lidar data, and critically evaluate and interpret reached results.

Teaching methods



The subject introduces the methods for digital processing of remotely sensed imagery. Explanation of concepts data corrections and transformations, methods of image segmentation, image filtration and using edge detectors, methods of image transformation into other coordinate systems, utlization of textural measures, pixel-based and object-based classification, soft classifiers, image spectroscopy, radar and lidar data processing.

Compulsory literature:

LIU J.G, MASON P.J. Image Processing and GIS for Remote Sensing. Willey, 2016. ISBN 9781118724200 LILLESAND T., KIEFER R., CHIPMAN J. Remote sensing and image interpretation. Wiley, 2015, 736 stran. ISBN: 978-1-118-34328-9 BLASCHKE, T., LANG, S., HAY, G. (Eds.). Object-Based Image Analysis. Springer Lecture Notes in Geoinformation and Cartography, 2008, XVII, 817 p. CHANG, N.-B., KAIXU, B. Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. CRC PRESS, S.l., 2020. s. 528. ISBN 978-0-367-57197-9.

Recommended literature:

SMITH, R. B. Analyzing hyperspectral data. Microimages, Inc., 2013. Dostupné na https://www.microimages.com/documentation/Tutorials/hypanly.pdf RICHARDS, J.A. Remote Sensing with Imaging Radar. Springer Verlag, 2009. ISBN: 3642020194. MOTT, H. Remote sensing with polarimetric radar. IEEE Press ; Wiley-Interscience, 2007. s. 309. ISBN 978-0-470-07476-3 CHUVIECO, E. Fundamentals of satellite remote sensing: an environmental approach, Second edition. ed. CRC Press, Taylor & Francis Group, Boca Raton, 2016. S. 468. ISBN 978-1-4987-2805-8

Way of continuous check of knowledge in the course of semester

Students are asked about knowledge from areas that they should have already known from previous lectures. Students also work on individual tasks. Tasks are frequently based on understanding of previous, simpler tasks.


Other requirements

No additional requirements are imposed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1. Digital image data from Remote sensing. Paradoxes of digital images, principles of segmentation. 2. Review of physical properties, spectral characteristics of landscape objects and phenomena and identification methods . 3. Preprocessing of digital images. Rectification methods. Radiometric and atmospherical corrections. Radiometric errors in data and its elimination. 5S model, Modtran, ATCOR 1-4, Sen2Cor, reflectivity calculation on the Earth surface, calculation of surface temperature. 4. Image enhancement methods. Thresholding, contrast modification, density slices, colour synthesis. Image filtration. Convolution. Filter separability. Low frequency filters, directional smoothing. 5. High frequency filters, edge operators, Laplacian operators, Canny detector, edge detection, pattern detection, edge delocalisation. 6. Texture, local textural measures (Haralick function), image segmentation methods (based on thresholding, edge detection, region based, hybrid methods), detection of geometric features, Hough transformation. 7. Object-based image analysis (OBIA). Utilization of segmentation, methods of delimitation of image objects, algorithms Baatz-Shäpe. Fourier transformation. 8. Spectral indeces. Variants of vegetation indeces, indeces for mineral detection, humidity, snow and others. Data fusion for images with different spatial resolution 9. Pixel-based clasification. Supervised spectral clasification of multispectral images. A training phase, correction of training sites. Parametric and non-parametric classifiers. 10. Pixel-based unsupervised classification methods. K-means, ISODATA, ISOCLUSTER. Hybrid clasification. Neural networks and advanced techniques of classification (deep learning, convolutional neural network). 11. Soft classifiers, utlization of Bayes theorem, Dempster-Shafer theory, classification and uncertainty. Assessment of classification results. Post-classification modification. 12. Image spectroscopy, hyperspectral and ultraspectral data. A review of sensors. Preprocessing and atmospherical corrections, flat field conversion, empirical line, models, PCA, MNF. Pixel purity index. End members and their discovering. Classification of hyperspectral data. Applications. 13. Processing data from radar systemms. Principles, biasesFactors influencing resulting signal. Coregistration of a pair of SAR products, interferogram creating and estimation of coherence, removal of demarcation from interferogram, image filtering. DInSAR method. Radar polarimetry. LIDAR and UAV. Processing of lidar data. Application fields.

Conditions for subject completion

Full-time form (validity from: 2021/2022 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33  17
        Examination Examination 67 (67) 18 3
                písemná zkouška Written examination 50  18
                ústní zkouška Oral examination 17  0
Mandatory attendence participation: Continuous check of processing tasks during exercises. Written and oral examination.

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Conditions for subject completion and attendance at the exercises within ISP: Materials for an individual study are available at http://homel.vsb.cz/~hor10/Vyuka/ where you can find also topics for the exam. Consultations (both personal and online) with the lecturer are possible. The exercises are individual based on a semester project which has to be completed to the end of the exam period for the given semester. The exam is conducted only in person.

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0532A330039) Geoinformatics GIT K Czech Ostrava 2 Compulsory study plan
2024/2025 (N0532A330039) Geoinformatics GIT P Czech Ostrava 2 Compulsory study plan
2023/2024 (N0532A330039) Geoinformatics GIT K Czech Ostrava 2 Compulsory study plan
2023/2024 (N0532A330039) Geoinformatics GIT P Czech Ostrava 2 Compulsory study plan
2022/2023 (N0532A330039) Geoinformatics GIT P Czech Ostrava 2 Compulsory study plan
2022/2023 (N0532A330039) Geoinformatics GIT K Czech Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

2023/2024 Winter