Prof. Ulrich Michel Profile

Prof. Ulrich Michel

Professor, Funding Strategy Manager at ROSENXT Creation Center GmbH

SPIE Involvement:

Conference Chair | Editor | Author

Publications (22)

Proceedings Article | 12 September 2021 Presentation + Paper

Remote sensing and GIS paves the way for depth of cover pipeline analysis

Ulrich Michel, Simon Daniels, Daniel Finley

Proceedings Volume 11863, 118630O (2021) https://doi.org/10.1117/12.2600110

KEYWORDS: Inspection, LIDAR, Remote sensing, Geographic information systems, Error analysis, Optical testing, Global Positioning System, Distance measurement, Databases, Surveillance

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Proceedings Article | 20 September 2020 Presentation

Opening Remarks by Conference Chairs

Karsten Schulz, Ulrich Michel, Konstantinos Nikolakopoulos

Proceedings Volume 11534, 1153403 (2020) https://doi.org/10.1117/12.2584100

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Proceedings Article | 9 October 2018 Paper

Detection of ditch structures in the region of "Altes Land" by means of current optical satellite data

Ulrich Michel, Anne Thieme

Proceedings Volume 10790, 107901I (2018) https://doi.org/10.1117/12.2325314

KEYWORDS: Satellites, Earth observing sensors, Landsat, Image segmentation, Scene classification, Near infrared, Spectral resolution, Geographic information systems, Remote sensing, Infrared radiation

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Proceedings Article | 18 October 2016 Paper

Environmental monitoring in peat bog areas by change detection methods

Ulrich Michel, Wiebke Mildes

Proceedings Volume 10005, 100051N (2016) https://doi.org/10.1117/12.2243059

KEYWORDS: Geographic information systems, Remote sensing, Environmental monitoring, Vegetation, Satellites, Clouds, Satellite imaging, Earth observing sensors, Image classification, Landsat

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Remote sensing image analysis systems and geographic information systems (GIS) show great promise for the integration of a wide variety of spatial information supporting tasks such as urban and regional planning, natural resource management, agricultural studies and topographic or thematic mapping. Current and future remote sensing programs are based on a variety of sensors that will provide timely and repetitive multisensor earth observation on a global scale. GIS offer efficient tools for handling, manipulating, analyzing and presenting spatial data that are required for sensible decision making in various areas. The Environmental Monitoring project may serve as a convincing example of the operational use of integrated GIS/remote sensing technologies. The overall goal of the project is to assess the capabilities of satellite remote sensing for the analysis of land use changes, especially in moor areas. These areas are recognized as areas crucial to the mission of the Department of Environment and, therefore, to be placed under an extended level of protection. It is of critical importance, however, to have accurate and current information about the ecological and economic state of these sensitive areas. In selected pasture and moor areas, methods for multisensor data fusion have being developed and tested. The results of this testing show which techniques are useful for pasture and moor monitoring at an operational level. A hierarchical method is used for extracting bog land classes with respect to the environmental protection goals. A highly accurate classification of the following classes was accomplished: deciduous- and mixed forest, coniferous forest, water, very wet areas, meadowland/farmland with vegetation, meadowland/farmland with partly vegetation, meadowland/ farmland without vegetation, peat quarrying with maximum of 50% vegetation, de- and regeneration stages. In addition, a change detection analysis is performed in comparison with the existing classification of 1994-96 [7] and methods are developed to improve the classification strategy.

Proceedings Article | 24 October 2013 Paper

GIS4schools: custom-made GIS-applications for educational use

Timo Demharter, Ulrich Michel

Proceedings Volume 8893, 889308 (2013) https://doi.org/10.1117/12.2027294

KEYWORDS: Geographic information systems, Geography, Information visualization, Prototyping, Satellite imaging, Earth observing sensors, Satellites, Visual analytics, Visualization, Network architectures

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Proceedings Article | 25 October 2012 Paper

Monitoring of changes in areas of conflicts: the example of Darfur

H. Thunig, U. Michel

Proceedings Volume 8538, 853803 (2012) https://doi.org/10.1117/12.978699

KEYWORDS: Algorithm development, Remote sensing, Buildings, Visualization, Earthquakes, Floods, Accuracy assessment, Standards development, Spatial resolution, Detection and tracking algorithms

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Proceedings Article | 25 October 2012 Paper

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

Fabian Löw, Gunther Schorcht, Ulrich Michel, Stefan Dech, Christopher Conrad

Proceedings Volume 8538, 85380R (2012) https://doi.org/10.1117/12.974588

KEYWORDS: Personal protective equipment, Agriculture, Vegetation, Feature selection, Remote sensing, Satellites, Near infrared, Data modeling, Algorithm development, Satellite imaging

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Proceedings Article | 25 October 2012 Paper

Integrated data processing of remotely sensed and vector data for building change detection

N. Sofina, M. Ehlers, U. Michel

Proceedings Volume 8538, 85380F (2012) https://doi.org/10.1117/12.975163

KEYWORDS: Geographic information systems, Satellite imaging, Satellites, Earth observing sensors, Raster graphics, Feature extraction, Chlorine, Data processing, Remote sensing, Earthquakes

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Proceedings Article | 25 October 2012 Paper

GIS4schools: A new approach in GIS education

Timo Demharter, Ulrich Michel

Proceedings Volume 8538, 85381T (2012) https://doi.org/10.1117/12.979706

KEYWORDS: Geographic information systems, Geography, Information visualization, Visualization, Web 2.0 technologies, Temperature metrology, Visual analytics, Network architectures, Networks, Telecommunications

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Proceedings Article | 27 October 2011 Paper

Object-based detection of destroyed buildings based on remotely sensed data and GIS

Natalia Sofina, Manfred Ehlers, Ulrich Michel

Proceedings Volume 8181, 81810D (2011) https://doi.org/10.1117/12.898469

KEYWORDS: Buildings, Raster graphics, Image segmentation, Geographic information systems, Earthquakes, Edge detection, Open source software, Computer programming, Algorithm development, Data processing

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Proceedings Article | 27 October 2011 Paper

Object-based detection of LUCC with special regard to agricultural abandonment on Tenerife (Canary Islands)

Sebastian Günthert, Alexander Siegmund, Holger Thunig, Ulrich Michel

Proceedings Volume 8181, 81811I (2011) https://doi.org/10.1117/12.897583

KEYWORDS: Agriculture, Image segmentation, Orthophoto maps, Image classification, Satellite imaging, Earth observing sensors, Satellites, Image processing, Climatology, Ecosystems

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Proceedings Article | 27 October 2011 Paper

Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

Timo Demharter, Ulrich Michel, Manfred Ehlers, Peter Reinartz

Proceedings Volume 8181, 81811J (2011) https://doi.org/10.1117/12.897589

KEYWORDS: Image segmentation, Accuracy assessment, Software development, Algorithm development, Remote sensing, Satellites, Buildings, Aluminum, Analytical research, Image processing

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Proceedings Article | 27 October 2011 Paper

Object-based rapid change detection for disaster management

Holger Thunig, Ulrich Michel, Manfred Ehlers, Peter Reinartz

Proceedings Volume 8181, 81810N (2011) https://doi.org/10.1117/12.897581

KEYWORDS: Algorithm development, Buildings, Remote sensing, Image segmentation, Accuracy assessment, Earthquakes, Floods, Vegetation, Earth observing sensors, Sensors

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Proceedings Article | 27 October 2011 Paper

Development of a satellite-based multi-scale land use classification system for land and water management in Uzbekistan and Kazakhstan

Fabian Löw, Ulrich Michel, Stefan Dech, Christopher Conrad

Proceedings Volume 8181, 81811K (2011) https://doi.org/10.1117/12.898038

KEYWORDS: Agriculture, Spatial resolution, Remote sensing, Satellites, Classification systems, Image classification, Earth observing sensors, Satellite imaging, Temporal resolution, Sensors

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Proceedings Article | 25 October 2010 Paper

Geoinformatics meets education for a peat bog information system

Ulrich Michel, Christina Fiene, Christian Plass

Proceedings Volume 7831, 78310U (2010) https://doi.org/10.1117/12.864989

KEYWORDS: Global Positioning System, Geoinformatics, Ecosystems, Visualization, Climatology, Remote sensing, Mobile devices, Document management, Multimedia, Carbon

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Proceedings Article | 13 October 2008 Paper

Geoinformatics paves the way for a zoo information system

Ulrich Michel

Proceedings Volume 7110, 71101B (2008) https://doi.org/10.1117/12.799817

KEYWORDS: Databases, Curium, 3D modeling, Panoramic photography, Geoinformatics, Document management, Buildings, Visualization, Remote sensing, Virtual reality

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Proceedings Article | 29 October 2007 Paper

MoZis: mobile zoo information system: a case study for the city of Osnabrueck

Ulrich Michel

Proceedings Volume 6749, 67490N (2007) https://doi.org/10.1117/12.738324

KEYWORDS: Visualization, Multimedia, Remote sensing, Databases, Global Positioning System, Mobile devices, Video, Prototyping, Human-machine interfaces, Geoinformatics

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Proceedings Article | 20 October 2006 Paper

GIS and remote sensing for 3D urban modeling by means of VRML technology

Ulrich Michel, Thorsten Bockmühl

Proceedings Volume 6366, 63660Q (2006) https://doi.org/10.1117/12.689927

KEYWORDS: Buildings, 3D modeling, Digital photography, Modeling, Geographic information systems, Photography, Remote sensing, Internet, Distortion, Image resolution

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A 3D representation of a city embedded in an Internet environment is not a new technology. It is already used in many projects which present tourist information. But 3D modelling assumes a lot of manual operations and these are still not fully operational to handle, even if there exist special software (e.g. CyberCity) nowadays, that support the generation of 3D scenes. The emphasis of this paper is to present a method for improving the automation of generating 3D VRML scenes by using simple and adapted procedures which are implemented in a GIS environment. Our goal was to create a backdrop which is easily modifiable and replaceable. Especially within a city we find areas (e.g. shop windows), whose appearance changes continuously. Their representations can be replaced by simple work steps in the 3D workflow. A 3D environment is based on a pool of different data e.g. cadastral maps, floor plans, and aerial images, which should have a minimum resolution of 50 cm, to guarantee the recognition of all important items. The cadastral maps should be controlled by using current aerial images to eliminate errors. More complex buildings are assembled from several simplified building sections. Subsequently, an automated modelling takes place after adding a few parameters (building height, roof form, etc.). For a realistic natural representation, the texturizing was assisted by using digital photographs of the building facade. The whole VRML scene is modular. Each wall was generated as an individual VRML file, turned and shifted into the suitable position. Every single wall can be addressed individually and this facilitates the adjustment of the textures enormously. In this way small changes of the buildings could be easily maintained without generating the whole VRML scene again. Finally, the modular city file was loaded into a main file with optional additives such as trees, viewpoints etc. This additional information could also be generated likewise automatically. For orientation purposes a general map was integrated, in which the position and line of sight of the user is indicated by an arrow. In conclusion, it can be stated that the proposed method and its implementation have been proved to be very valuable and reliable for automated 3D urban modelling.

Proceedings Article | 29 October 2005 Paper

Advanced automated classification strategy for settlement area detection

Ulrich Michel

Proceedings Volume 5983, 598308 (2005) https://doi.org/10.1117/12.627034

KEYWORDS: Image segmentation, Geographic information systems, Vegetation, Remote sensing, Satellites, Data modeling, Landsat, Global Positioning System, Agriculture, Earth observing sensors

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Proceedings Article | 22 October 2004 Paper

Advanced GPS-based field mapping for collecting training data within a remote sensing classification approach

Ulrich Michel

Proceedings Volume 5574, (2004) https://doi.org/10.1117/12.565495

KEYWORDS: Global Positioning System, Associative arrays, Image classification, Remote sensing, Geographic information systems, Satellites, Receivers, Software development, Visualization, Earth observing sensors

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Automation of image classification is a challenge to the image interpretation community. One of the most time consuming task is certainly the collection of training data. The introduction especially of low cost Global Positioning System (GPS) receivers and the higher accuracy of the GPS signal after turning-off the Selective Availability has enhanced the ease and versatility of spatial data acquisition. Has also made the approaches by which it is integrated with GIS and remote sensing data more flexible. The emphasis of this paper is to present a method for improving the training data collection for classification purposes using remotely sensed imagery as well as various data sources combined in a GIS. Firstly, a methodology for defining and describing training areas is demonstrated. The training data were stored in a vector data base (shape files) by using the geometry of the land parcels of the test site. Secondly, in addition to a conventional field mapping approach, an advanced GPS based field mapping methodology was used to collect new training data. Within this new approach single point information of the target ground truth class were collected along the roads in this test area. In this step, the following attributes were recorded: ID, left or right of the street and biotope class. The goal for this approach is that one single person should handle the field mapping while driving in a car. The implementation of this approach is performed in ArcPad 6.03 and Application Builder from ESRI. The standard version of ArcPad was modified so that a one hand collection of training data is possible. After the field survey, the results were used within Erdas Imagine (version 8.7). In our approach all "left" points were moved to the adjacent left field - "orthogonal" to the street. All "right" points were shifted to the adjacent right field. Now those moved points were used as a seed pixel in a Euclidian distance algorithm to automatically derive new training sets. In conclusion it can be stated that the proposed training collecting method and its implementation have been proved to be a very valuable and reliable method for image classification purposes.

Proceedings Article | 13 February 2004 Paper

Automated knowledge-based land use monitoring by remote sensing and GIS integration

Ulrich Michel

Proceedings Volume 5239, (2004) https://doi.org/10.1117/12.511470

KEYWORDS: Landsat, Image processing, Geographic information systems, Image classification, Remote sensing, Global Positioning System, Databases, Classification systems, Environmental sensing, Earth observing sensors

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The emphasis of this paper is on an improvement of the classification procedure for change detection purposes using remotely sensed imagery as well as various data sources combined in a GIS. Our contributions will be on an advanced knowledge-based methodology for the training phase within the change detection process, as well as on the evaluation of the applied software package (Erdas Imagine Expert Classifier) and of the used image data source, namely Landsat TM 7 data. Firstly, we will demonstrate an improved and flexible methodology for defining and describing training areas in the course of a change detection process using knowledge-based image analysis techniques. Using the GIS-database which comprises several data sources at point of time t0 the outlines of the desired object classes will be determined and rated according to their accuracy respectively reliability. Combining this information with the image data of the first phase (t1), we are entering the first training stage. Here, not only a single standard object signature like a multispectral reflectance but a large amount of additional parameters are checked. For each parameter the inference mechanism automatically checks the separability for different object classes and evaluates the suitability of each signature for further use within the first classification stage. The reliability of an object is derived from all information stored in the initial database. As an output of the classification stage -- again applying knowledge-based rules -- we obtain probability vectors which decide in favor of a confirmation, a modification or an elimination of the given outlines for the specific class. The new outlines together with the imagery of the next acquisition phase and up-dated ancillary data are put into the next training phase. Due to possibly changed image properties it is meaningful to test all signatures for the given outlines again, and proceed as described above. The implementation of this knowledge-based approach is performed by means of the "Expert Classifier" from Erdas Imagine (version 8.6). In conclusion it can be stated that the proposed knowledge-based method and its implementation has been proved to be a very valuable and reliable method for environmental change detection purposes.

Proceedings Article | 23 January 2002 Paper

Advanced knowledge-based methodology for the training phase within the classification process of remote sensing imagery

Ulrich Michel

Proceedings Volume 4545, (2002) https://doi.org/10.1117/12.453660

KEYWORDS: Image processing, Databases, Remote sensing, Image classification, Geographic information systems, Landsat, Satellites, Environmental sensing, Associative arrays, Earth observing sensors

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Showing 5 of 22 publications

Proceedings Volume Editor (20)

SPIE Conference Volume | 27 November 2023

Earth Resources and Environmental Remote Sensing/GIS Applications XIV

SPIE Conference Volume | 15 November 2022

Earth Resources and Environmental Remote Sensing/GIS Applications XIII

SPIE Conference Volume | 6 October 2021

Earth Resources and Environmental Remote Sensing/GIS Applications XII

SPIE Conference Volume | 6 October 2020

Earth Resources and Environmental Remote Sensing/GIS Applications XI

SPIE Conference Volume | 4 November 2019

Earth Resources and Environmental Remote Sensing/GIS Applications X

Showing 5 of 20 publications

Conference Committee Involvement (23)

Earth Resources and Environmental Remote Sensing/GIS Applications XV

16 September 2024 | Edinburgh, United Kingdom

Earth Resources and Environmental Remote Sensing/GIS Applications XIV

3 September 2023 | Amsterdam, Netherlands

Earth Resources and Environmental Remote Sensing/GIS Applications XIII

5 September 2022 | Berlin, Germany

Earth Resources and Environmental Remote Sensing/GIS Applications XII

13 September 2021 | Online Only, Spain

Earth Resources and Environmental Remote Sensing/GIS Applications XI

22 September 2020 | Online Only, United Kingdom

Earth Resources and Environmental Remote Sensing/GIS Applications X

10 September 2019 | Strasbourg, France

Earth Resources and Environmental Remote Sensing/GIS Applications

11 September 2018 | Berlin, Germany

Earth Resources and Environmental Remote Sensing/GIS Applications

12 September 2017 | Warsaw, Poland

Earth Resources and Environmental Remote Sensing/GIS Applications

27 September 2016 | Edinburgh, United Kingdom

Earth Resources and Environmental Remote Sensing/GIS Applications

22 September 2015 | Toulouse, France

Earth Resources and Environmental Remote Sensing/GIS Applications

23 September 2014 | Amsterdam, Netherlands

SPIE Remote Sensing

22 September 2014 | Amsterdam, Netherlands

Earth Resources and Environmental Remote Sensing/GIS Applications IV

23 September 2013 | Dresden, Germany

SPIE Remote Sensing

23 September 2013 | Dresden, Germany

Earth Resources and Environmental Remote Sensing/GIS Applications

25 September 2012 | Edinburgh, United Kingdom

Earth Resources and Environmental Remote Sensing/GIS Applications

20 September 2011 | Prague, Czech Republic

Earth Resources and Environmental Remote Sensing/GIS Applications

21 September 2010 | Toulouse, France

Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX

31 August 2009 | Berlin, Germany

Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII

15 September 2008 | Cardiff, Wales, United Kingdom

Remote Sensing for Environmental Monitoring, GIS Applications, and Geology

17 September 2007 | Florence, Italy

Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI

13 September 2006 | Stockholm, Sweden

Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V

19 September 2005 | Bruges, Belgium

Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III

9 September 2003 | Barcelona, Spain

Showing 5 of 23 Conference Committees

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