Remote Sensing is the most valuable source for the acquisition of actual geodata, e.g. land use / land cover (LULC). Remotely sensed imagery is acquired by high resolution spaceborne sensors with ground resolutions up to 0.6m, airborne imagery nowadays provide spatial resolution better than 0.1m. Both types of imagery record the reflected electromagnetic energy in several spectral bands, ranging from the visible wavelength to the near infrared.This Remote Sensing module follows a multi step education, the typical workflow of remote sensing process: recording, processing, analyzing, and applying. The introduction gives a fundamental background about the theory of spectral data origin and its digital acquisition first. Operational sensors and platforms available for data acquisition will be described as well. Remotely sensed data processing means the elimination of system errors and the georeferencing of the image data. A very important part in the process is the data analysis, that is generating information from raw remote sensing data, such as extracting real world objects or mapping land use land coverage. Therefore, in principle two different methods are available: a statistical pixel-per-pixel approach and the more sophisticated object-based image analysis. The results of image analysis in general are transferred into and stored in a Geographical Information System (GIS), where they can be combined with additional data for a more advanced modelling and visualization. Vice versa, already existing GIS data can support the image analysis process.
- Explain the relationship between electromagnetic radiation, geo objects and the generation of geo information
- Give insight into different kinds of sensors, systems and satellite platforms
- Explain where and how to obtain remote sensing data
- Explain how to handle different kinds and formats of data
- Demonstrate and enable student to apply methods and the typical workflow of processing remote sensing data for information extraction
- Enable students to assess data and valuate analysis results for typical application purposes
Demonstrate typical examples for applied remote sensing
Dr. Peter Hofmann
Austrian Academy of Sciences
Geographic Information Science (ÖAW)
Schillerstr. 30, 5020 Salzburg
Dr. Peter Hofmann works in the field of Remote Sensing for geo-information retrieval, Object-based Image Analysis (OBIA), Integration and formalization of (spatio-temporal) expert knowledge in OBIA. He is expert in investigating methods for assessing the robustness and transferability of methods for image analysis. His is well versed in Geography & Geoinformatics, 2D, 3D and 4D modeling.
We would like to inform you that this is an exclusively english language module, hence any kind of communication with the module instructor should be in English. A discussion forum is maintained in Blackboard in order to support efficient module instruction. You are requested to submit all your questions related to this module to this forum only. The instructor will check all incoming comments on a regular basis. He will answer your questions or provide you with pointers for solving your problems. The module is delivered in form of an instructed self-study that is based on explorative learning process and process. Theoretical concepts are complemented with practice oriented examples demonstrated with help of multimedia elements. Upon completion of the module students are requested to evaluate the module, which is a part of our quality assurance policy and practice.
Software and Literature
ERDAS Imagine 2014; eCognition Developer Trial (64-bit), ...
Literature: LILLESAND T./KIEFER R., 2008, 6th Ed.,:Remote sensing and image interpretation. New York.
Assessment and Grading
The assessment is based on your completed assignments. They must be submitted in written format (.PDF/.DOC) to the Dropbox within the required time period. If assignments are submitted late, the instructor is not obligated to grade them. It will be listed as such on your transcript.
|Start:||3 x per year|
|Registration deadline:||two weeks before start|
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