548-0024/03 – Digital Processing of Remotely Sensed Data (DZDPZ)
| Gurantor department | Department of Geoinformatics | Credits | 5 |
| Subject guarantor | prof. Ing. Jiří Horák, Dr. | Subject version guarantor | Ing. Tomáš Peňáz, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 4 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 1999/2000 | Year of cancellation | 2016/2017 |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
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
Other requirements
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. 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 and its review.
- Radiometric, spatial, spectral enhancement of remote 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 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. Using artificial intelligence. Object-oriented image classification.
- Hyperspectral image processing.
- Radar sensed image processing.
- Integration of remote sensed data with GIS.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction