Dr. Sebastian Fritsch Profile

Dr. Sebastian Fritsch

at IDEMIA

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Publications (5)

Proceedings Article | 12 May 2016 Paper

Detecting liquid threats with x-ray diffraction imaging (XDi) using a hybrid approach to navigate trade-offs between photon count statistics and spatial resolution

Sondre Skatter, Sebastian Fritsch, Jens-Peter Schlomka

Proceedings Volume 9847, 984704 (2016) https://doi.org/10.1117/12.2228953

KEYWORDS: X-rays, Liquids, X-ray diffraction, Photon counting, Sensors, Signal attenuation, Imaging systems, Image segmentation, Scatter measurement, Collimation

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Proceedings Article | 19 October 2012 Paper

Crop classification at subfield level using RapidEye time series and graph theory algorithms

Gunther Schorcht, Fabian Löw, Sebastian Fritsch, Christopher Conrad

Proceedings Volume 8531, 85311G (2012) https://doi.org/10.1117/12.974670

KEYWORDS: Image segmentation, Satellites, Image processing algorithms and systems, Satellite imaging, Image classification, Remote sensing, Vegetation, Earth observing sensors, Agriculture, Personal protective equipment

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Accurate information about land use patterns is crucial for a sustainable and economical use of water in agricultural systems. Water demand estimation, yield modeling and agrarian policy are only a few applications addressed by land use classifications based on remote sensing imagery. In Central Asia, where fields are traditionally large and state order crops dominate the area, small units of fields are often separated for the additional cultivation of income crops for the farmers. Traditional object based land use classifications on multi-temporal satellite imagery using field boundaries show low classification accuracies on these separated fields, expressed by a high uncertainty of the final class labels. Although segmentation of smaller subfields was shown to be suitable for improving the classification result, the extraction of subfields is still a time-consuming and error-prone process. In this study, energy based Graph-Cut segmentation technique is used to enhance the segmentation process and finally to improve the classification result. The interactive segmentation technique was successfully adopted from bio-medical image analysis to fit remote sensing imagery in the spatial and in the temporal domain. A set of rules was developed to perform the image segmentation procedure on pixels of single satellite datasets and on objects representing time series of a vegetation index. An ensemble classifier based on Random Forest and Support Vector Machines was used to receive information about classification uncertainty before and after applying the segmentation. It is demonstrated that subfield extraction based on Graph Cuts outperforms traditional image segmentation approaches in simplicity and reduces the risk of under- and over-segmentation significantly. Classification uncertainty decreased using the derived subfields as object boundaries instead of original field boundaries. The segmentation technique performs well on several multi-temporal satellite images without changing parameters and may be used to refine object based land use classifications to subfield level.

Proceedings Article | 7 October 2011 Paper

Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia

Christopher Conrad, Miriam Machwitz, Gunther Schorcht, Fabian Löw, Sebastian Fritsch, Stefan Dech

Proceedings Volume 8174, 817412 (2011) https://doi.org/10.1117/12.898345

KEYWORDS: Agriculture, Vegetation, Remote sensing, Data acquisition, Associative arrays, Classification systems, Composites, Spatial resolution, Algorithm development, Image classification

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Proceedings Article | 7 October 2011 Paper

Relationships between high resolution RapidEye based fPAR and MODIS vegetation indices in a heterogeneous agricultural region

Sebastian Fritsch, Miriam Machwitz, Christopher Conrad, Stefan Dech

Proceedings Volume 8174, 81740N (2011) https://doi.org/10.1117/12.898333

KEYWORDS: MODIS, Vegetation, Agriculture, Composites, Statistical analysis, Reflectivity, Remote sensing, Satellites, Sensors, Data modeling

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Proceedings Article | 22 October 2010 Paper

Statistical derivation of fPAR and LAI for irrigated cotton and rice in arid Uzbekistan by combining multi-temporal RapidEye data and ground measurements

Andrea Ehammer, Sebastian Fritsch, Christopher Conrad, John Lamers, Stefan Dech

Proceedings Volume 7824, 782409 (2010) https://doi.org/10.1117/12.864796

KEYWORDS: Vegetation, Reflectivity, Satellites, Image segmentation, Sensors, Remote sensing, Satellite imaging, Biological research, Earth observing sensors, In situ metrology

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