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Advanced methods in Remote Sensing

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Course Unit Code548-0146/01
Number of ECTS Credits Allocated5 ECTS credits
Type of Course Unit *Compulsory
Level of Course Unit *Second Cycle
Year of Study *Second Year
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
HOR10prof. Ing. Jiří Horák, Dr.
Summary
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.
Learning Outcomes of the Course Unit
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.
Course Contents
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.
Recommended or Required Reading
Required Reading:
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.
HORÁK, J. Dálkový průzkum Země. Ostrava : Vysoká škola báňská - Technická univerzita Ostrava, 2014. 137 s.
HALOUNOVÁ, L.; PAVELKA, K. Dálkový průzkum Země. Vydavatelství ČVUT. Praha, 2005.
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
Recommended Reading:
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
DOBROVOLNÝ P. Dálkový průzkum Země. Digitální zpracování obrazu. Masarykova univerzita, 1998.
BLASCHKE, T., LANG, S., HAY, G. (Eds.). Object-Based Image Analysis. Springer Lecture Notes in Geoinformation and Cartography, 2008, XVII, 817 p.
RICHARDS, J.A. Remote Sensing with Imaging Radar. Springer Verlag, 2009. ISBN: 3642020194.
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
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Credit and ExaminationCredit and Examination100 (100)51
        CreditCredit33 17
        ExaminationExamination67 (67)18
                písemná zkouškaWritten examination50 18
                ústní zkouškaOral examination17 0