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
HOR10 doc. Ing. Jiří Horák, Dr.
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

Raster data gathered using remote sensing methods. N-dimensional image data. Elementary descriptive statistics. Spatial statistics for remote sensing. Multiple linear regression. Image data format, import and export. Image file format conversion. Remote sensing image data overview. Digital image data errors. Image data pre-processing. Radiometric and atmospheric image correction. Image enhancement, the main aim of image enhancement techniques. Radiometric, spatial, spectral enhancement of remotely sensed data. Multispectral image processing. Image geometric transformation. Image registration and the removal of geometric distortion. Numeric transformation, polynomial equations, ground control points, transformation matrix. Image moving, scale, rotation, resampling. Extracting information from an image. Visual interpretation and automated classification. Main approaches to image classification. Classification rules. Using spectral classification rules in a supervised or unsupervised process. Parametric and non-parametric classification rules. Evaluating of automated image classification. Comparing semi-automated classification and visual interpretation methods. Complementary classification approaches for image processing. Contextual classification, Automated Change Detection and Classification. Fuzzy image classification. Using artificial intelligence. Object-oriented image classification. Hyperspectral image processing. Radar sensed image processing. Integration of remotely sensed data with GIS.

Compulsory literature:

Avery, T.E.; Berlin, G.L.: Fundamentals of Remote Sensing and Airphoto Interpretation. Pearson Prentice Hall, 1992. 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. WARNER, T.A.; CAMPAGNA, D.J.: Remote Sensing with IDRISI. A Beginner's Guide. Geocarto International Centre, 2013.

Recommended literature:

Landgrebe, D.: Information Extraction Principles and Methods for multispectral and Hyperspectral Image Data. School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN 47907-1285. On-line http://dynamo.ecn.purdue.edu/~landgreb/publications.html 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

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:

- Raster data gathered using remote sensing methods. N-dimensional image data. Elementary descriptive statistics. Spatial statistics for remote sensing. Multiple linear regression. - Image data format, import and export. Image file format conversion. Remotely sensed image data overview. - Digital image data errors. Image data pre-processing. Radiometric and atmospheric image correction. - Image enhancement. The main aim of image enhancement techniques and its review. - Radiometric, spatial, spectral enhancement of remote sensed data. Multispectral image processing. - Image geometric transformation. Image registration and the removal geometric distortion. Numeric transformation, polynomial equations, ground control points, transformation matrix. Image moving, scale, rotation, resampling. - Extracting information from image. Visual interpretation and automated classification. Main approaches to image classification. Classification rules. - Using spectral classification rules in supervised or unsupervised process. Parametric and non-parametric classification rules. Evaluating of automatomated image classification. Comparing semi-automated classification and visual interpretation methods. - Complementary classification approaches for image processing. Contextual classification, automated change detection and classification, fuzzy image classification. Information extraction using artificial intelligence. Object based image classification. - Hyperspectral image processing. - Radar sensed image processing. - Integration of remotely sensed data with GIS.

Conditions for subject completion

Combined form (validity from: 2017/2018 Winter 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  17
        Examination Examination 67  18
Mandatory attendence parzicipation: The range of compulsory attendance at the on-site form of study is at least half of the planned range per semester.

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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