548-0024/06 – Digital Processing of Remotely Sensed Data (DZDPZ)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantorIng. Tomáš Peňáz, Ph.D.Subject version guarantorIng. Tomáš Peňáz, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year1Semesterwinter
Study languageEnglish
Year of introduction2015/2016Year of cancellation
Intended for the facultiesHGFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
PEN63 Ing. Tomáš Peňáz, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 6+6

Subject aims expressed by acquired skills and competences

The main aim of this subject is to introduce students into the digital processing of remotely sensed data. This know-how can be used as a tool within some subjects studies. The course participant understands how to practically use these digital image processing methods. He achieves critically assess this processing outcome.

Teaching methods

Lectures
Tutorials
Project work

Summary

The subject introduces the methods for digital processing of remotely sensed imagery. The subject has a practical orientation, combining conceptual foundations with a view towards applications. Students are offered a selection of advanced processing techniques for remotely sensed imagery. The course participant manages to choose the appropriate processing method, comprehends how to use the method practically and is able to assess the processing outcomes critically.

Compulsory literature:

Avery, T.E.; Berlin, G.L.: Fundamentals of Remote Sensing and Airphoto Interpretation. Pearson Prentice Hall, 1992. Eastman, J. R.: IDRISI Selva Tutorial (Part 4 a Part 5), Manual Version 17.01, Clark University, 2012. Jensen, J.R.: Introductory Digital Image Processing: A Remote Sensing Perspective. Pearson Prentice Hall, 2005, ISBN-13: 978-0131453616 Lillesand, T.; Kiefer, R.: Remote sensing and image interpretation. John Wiley & Sons, 1994. Smith, R. B.: Analyzing hyperspectral data. Microimages, Inc., 2013, on-line https://www.microimages.com/documentation/Tutorials/hypanly.pdf Warner, T.A.; Campagna, D.J.: Remote Sensing with IDRISI. A Beginner's Guide. Geocarto International Centre, 2013.

Recommended literature:

Landgrebe, D.: On Information Extraction Principles for Hyperspectral Data: A White Paper. School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN 47907-1285. On-line https://engineering.purdue.edu/~landgreb/whitepaper.pdf Schott, J.R.: Remote Sensing. The Image Chain Approach. Oxford University, 1997.

Way of continuous check of knowledge in the course of semester

E-learning

https://lms.vsb.cz/?lang=en

Další požadavky na studenta

No additional requirements are imposed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Remote sensing image, properties, structure. Types of numerical data and their conversion. Raster data formats, import and export raster data, conversion of data formats. 2. Remote sensing image pre-processing. Atmospheric correction, terrain relief, and cirrus. The optical thickness of the atmosphere, relative and absolute atmospheric correction of image data. A complete model of electromagnetic radiation transmitted through the atmosphere. Modeling of terrain relief and cirrus influence on electromagnetic radiation in the atmosphere. Tools for atmospheric influence modeling (ATREM, ATMOSC, ATCOR2,3, Sen2cor,…). 3. Spectral indices from multispectral data. Ratio-based indices, orthogonal indices, distance-based indices. Application of spectral indices for vegetation studies, in geology, for identification and evaluation of fire, other spectral indices. Index database. 4. Supervised classification, classification scheme. Training stage, in-situ data collection and data acquisition from alternative sources. Training stage evaluation, correction of training areas. Parametric and non-parametric classifiers. Ground truthing Importance of reference data in the evaluation of classification success. Post-classification processing. 5. Comparing visual interpretation with computer-based image classification. Unsupervised classification. Clustering algorithms RGB clustering, K-means, ISODATA, ISOCLUSTER, Narendra-Goldberg, EM clustering. Transformation of spectral classes into information classes. Classification result adjustment based on the classification tree. The use of clusters for the hybrid classification technique. Evaluation of computer-based classification results. 6. Object-based analysis (OBIA). Methods of segmentation, methods of delimitation of image objects (watershed delineation approach, Baatz-Shäpe algorithm). 7. Identification of changes in the landscape, pairwise comparisons (simple differences, image regression, image rationing) and multiple comparisons - time-series analyses. Change mapping based on SAR data. 8. Complementary methods of classification. Bayes' theorem and maximum likelihood classification. Classification based on temporal changes in a landscape. Soft classification methods based on Bayes' theorem and maximum likelihood classification, Dempster-Shafer theory, Mahalanobis distance, fuzzy sets. Utilization of uncertainty theory in classification. Use of context and texture in classification. 9. Image spectrometry data processing. 10. Utilization of artificial intelligence, machine learning, a neural network for remote sensing image processing. Deep learning technique for image data processing. 11. Methods of thermal image processing from remote sensing. Images from thermoelectric, bolometric and quantum sensors. Thermal image visualization, thermogram. High-resolution thermal image interpretation and identification of thermal anomalies in a thermal image. Thermometry. 12. Processing of image data from radar systems. Co-registration of a pair of SAR products, interferogram creating and estimation of coherence, removal of demarcation from interferogram, image filtering. DInSAR method. Radar polarimetry. Mapping of land cover based on SAR image classification. 13. Methods of remote sensing for measuring the height and mapping of water objects. Radar Altimetry. Utilization of sonar. 14. Integration of remote sensing data into GIS.

Conditions for subject completion

Full-time form (validity from: 2017/2018 Winter semester, validity until: 2017/2018 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33 (33) 17
                Project Semestral project 13  6
                Test Other task type 13  6
                Attendance at tutorials Other task type 7  5
        Examination Examination 67 (67) 25
                Written test Written examination 40  18
                Oral exam Oral examination 27  0
Mandatory attendence parzicipation: The range of compulsory attendance at tutorials is set at the interval of 5 - 7 points per semester. Each active attendance at one tutorial session - a half-point evaluation.

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Compulsory study plan
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Compulsory study plan
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Compulsory study plan
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Compulsory study plan
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Compulsory study plan
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Compulsory study plan
2016/2017 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Compulsory study plan
2015/2016 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner