12 results on '"Martinis, Sandro"'
Search Results
2. The International Charter ‘Space and Major Disasters’: DLR’s Contributions to Emergency Response Worldwide
- Author
-
Martinis, Sandro, Twele, André, Plank, Simon, Zwenzner, Hendrik, Danzeglocke, Jens, Strunz, Günter, Lüttenberg, Hans-Peter, and Dech, Stefan
- Published
- 2017
- Full Text
- View/download PDF
3. Improving SAR-based flood detection in arid regions using texture features
- Author
-
Ritushree, Dk, Garg, Shagun, Dasgupta, Antara, Martinis, Sandro, Selvakumaran, Sivasakthy, and Motagh, Mahdi
- Subjects
flood mapping ,arid areas ,texture features ,SAR - Published
- 2023
4. Strategies for the Automatic Extraction of Water Bodies from TerraSAR-X / TanDEM-X data
- Author
-
Hahmann, Thomas, Twele, André, Martinis, Sandro, Buchroithner, Manfred, Konecny, Milan, editor, Zlatanova, Sisi, editor, and Bandrova, Temenoujka L., editor
- Published
- 2010
- Full Text
- View/download PDF
5. Flood mapping from space
- Author
-
Martinis, Sandro, Wieland, Marc, and Bettinger, Michaela
- Subjects
Remote Sensing ,Insurance ,Flood Mapping - Published
- 2018
6. A Sentinel-1 Times Series-Based Exclusion Layer for Improved Flood Mapping in Arid Areas
- Author
-
Martinis, Sandro
- Subjects
Synthetic aperture radar ,021110 strategic, defence & security studies ,Flood myth ,Backscatter ,time-series ,Reliability (computer networking) ,0211 other engineering and technologies ,02 engineering and technology ,arid Areas ,Arid ,law.invention ,flood mapping ,Variable (computer science) ,law ,Sentinel-1 ,Environmental science ,sand surfaces ,Radar ,Layer (object-oriented design) ,021101 geological & geomatics engineering ,Remote sensing - Abstract
Due to the similarity of the radar backscatter over open water and sand surfaces a reliable near real-time flood mapping based on radar sensors in arid areas is usually not possible. Within this paper an approach is presented to enhance the results of an automatic Sentinel-l flood processing chain of the German Aerospace Center (DLR) by removing overestimations of the water extent related to sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series information of Sentinel-l data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia, which has been covered by a time-series of 202 Sentinel-l scenes within the period April 2014 to May 2017. The algorithm proved capable to significantly improving the classification accuracy of the Sentinel-l flood service at this study site. Experimental results with variable lengths of the time-series have shown that the classification accuracy increased with increasing number of data sets at the cost of higher computational demand.
- Published
- 2018
7. Inundation mapping using C- and X-band SAR data: From algorithms to fully-automated flood services
- Author
-
Twele, André, Martinis, Sandro, Cao, Wenxi, and Plank, Simon
- Subjects
flood mapping ,rapid mapping ,Sentinel-1 ,Georisiken und zivile Sicherheit ,TerraSAR-X ,automation - Abstract
Twele, André; Sandro, Martinis; Wenxi, Cao; Simon, Plank German Aerospace Center (DLR), Germany Since the establishment of the ZKI (Center for Satellite-Based Crisis Information) at the German Aerospace Center (DLR), the development of EO-based methodologies for the rapid mapping of flood situations has been of major concern. This can be especially contributed to the fact that inundations constitute the majority of all ZKI-activations as well as activations of the International Charter ‘Space and Major Disasters’. These requirements have led to the development of dedicated SAR-based flood mapping tools which have been utilized during numerous rapid mapping activities of flood situations. The core of these tools is an automatic tile-based thresholding approach (Martinis et al. 2009, 2011, Martinis and Twele 2010) which allows separating inundated regions from land-areas without any user interaction. Recently, the SAR-based flood detection algorithm has been substantially extended and refined in robustness and transferability to guarantee high classification accuracy under different environmental conditions and sensor configurations with the ultimate goal to allow its implementation in an automatic processing chain (Martinis et al. 2014). The processing chain including SAR data pre-processing, computation and adaption of global auxiliary data, unsupervised initialization of the classification as well as post-classification refinement by using a fuzzy logic-based approach is automatically triggered after new SAR data is available on a delivery server. The dissemination of flood maps resulting from the service is performed through a dedicated web client. With respect to accuracy and computational effort, experiments performed on a data set of >200 different TerraSAR-X scenes acquired during flooding all over the world with different sensor configurations confirmed the robustness and effectiveness of the flood mapping service. The processing chain has recently been adapted to the new European Space Agency’s C-band SAR mission Sentinel-1. The thematic processor has further been enhanced through the integration of the “Height above nearest drainage index” (Rennó et al. 2008) which helps to reduce water look-alikes depending on the hydrologic-topographic setting. In contrast to the current TerraSAR-X based thematic service, Sentinel-1 enables a systematic disaster monitoring with high spatial and temporal resolutions. This is a major advantage since the time-consuming step of tasking new satellite data can be omitted. By minimizing the time delay between data delivery and product dissemination it is expected that the proposed service enhances the value of remote sensing during flood management activities and supports applications in hydrology, where information about the flood extent is systematically assimilated into hydrologic and hydraulic models. The presentation will introduce to the technical concept of the SAR-based fully-automated processing chains, with a focus on the current status of the Sentinel-1 flood service. References: Martinis, S., Twele, A. & Voigt, S. 2009: Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Natural Hazards and Earth System Sciences (NHESS), 9, 303-314. Martinis, S. & Twele, A. 2010: A Hierarchical Spatio-Temporal Markov Model for Improved Flood Mapping Using Multi-Temporal X-Band SAR Data. Remote Sensing, 2 (9), 2240-2258. 46 Martinis, S., Twele, A. & Voigt, S. 2011: Unsupervised extraction of flood-induced backscatter changes in SAR data using Markov image modeling on irregular graphs. IEEE Transactions on Geoscience and Remote Sensing, 49 (1), 251-263. Martinis, S., Twele, A. & Kersten, J. 2014 (in press): A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing. Rennó, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G., Tomasella, J. & Waterloo, M. J. 2008: HAND, a new terrain descriptor using SRTM-DEM: Mapping terrafirme rainforest environments in Amazonia. Remote Sensing of Environment, 112 (9), 3469-3481.
- Published
- 2015
8. Extraction of water and flood areas from SAR data
- Author
-
Hahmann, Thomas, Martinis, Sandro, Twele, André, Roth, Achim, Buchroithner, Manfred, and ITG/VDE
- Subjects
flood mapping ,Umwelt und Sicherheit ,image analysis ,Synthetic aperture radar ,TerraSAR-X ,Water body detection - Abstract
Medium resolution SAR satellite data have been widely used for water and flood mapping in recent years. Since 2007 high resolution radar data with up to 1 m pixel spacing of the TerraSAR-X satellite are operationally avail-able. The improved ground resolution of the system offers enormous potential for water detection. However, im-age analysis gets more challenging due to the large amount of image objects that are visible in the data. Water body detection methods are reviewed with regard to their applicability for TerraSAR-X data. Flood detection approaches for rapid disaster mapping are presented in this paper.
- Published
- 2009
9. Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs.
- Author
-
Martinis, Sandro, Twele, André, and Voigt, Stefan
- Subjects
- *
SYNTHETIC aperture radar , *FLOODS , *DATA extraction , *BACKSCATTERING , *MARKOV processes , *DATA modeling , *GRAPH theory , *COMPUTER simulation - Abstract
The near real-time provision of precise information about flood dynamics from synthetic aperture radar (SAR) data is an essential task in disaster management. A novel tile-based parametric thresholding approach under the generalized Gaussian assumption is applied on normalized change index data to automatically solve the three-class change detection problem in large-size images with small class a priori probabilities. The thresholding result is used for the initialization of a hybrid Markov model which integrates scale-dependent and spatiocontextual information into the labeling process by combining hierarchical with noncausal Markov image modeling. Hierarchical maximum a posteriori (HMAP) estimation using the Markov chains in scale, originally developed on quadtrees, is adapted to hierarchical irregular graphs. To reduce the computational effort of the iterative optimization process that is related to noncausal Markov models, a Markov random field (MRF) approach is defined, which is applied on a restricted region of the lowest level of the graph, selected according to the HMAP labeling result. The experiments that were performed on a bitemporal TerraSAR-X StripMap data set from South West England during and after a large-scale flooding in 2007 confirm the effectiveness of the proposed change detection method and show an increased classification accuracy of the hybrid MRF model in comparison to the sole application of the HMAP estimation. Additionally, the impact of the graph structure and the chosen model parameters on the labeling result as well as on the performance is discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
10. A Hierarchical Spatio-Temporal Markov Model for Improved Flood Mapping Using Multi-Temporal X-Band SAR Data.
- Author
-
Martinis, Sandro and Twele, André
- Subjects
- *
MARKOV processes , *FLOOD forecasting , *SYNTHETIC aperture radar , *MARKOV random fields , *ENTROPY - Abstract
In this contribution, a hybrid multi-contextual Markov model for unsupervised near real-time flood detection in multi-temporal X-band synthetic aperture radar (SAR) data is presented. It incorporates scale-dependent, as well as spatio-temporal contextual information, into the classification scheme, by combining hierarchical marginal posterior mode (HMPM) estimation on directed graphs with noncausal Markov image modeling related to planar Markov random fields (MRFs). In order to increase computational performance, marginal posterior-based entropies are used for restricting the iterative bi-directional exchange of spatio-temporal information between consecutive images of a time sequence to objects exhibiting a low probability, to be classified correctly according to the HMPM estimation. The Markov models, originally developed for inference on regular graph structures of quadtrees and planar lattices, are adapted to the variable nature of irregular graphs, which are related to information driven image segmentation. Entropy based confidence maps, combined with spatio-temporal relationships of potentially inundated bright scattering vegetation to open water areas, are used for the quantification of the uncertainty in the labeling of each image element in flood possibility masks. With respect to accuracy and computational effort, experiments performed on a bi-temporal TerraSAR-X ScanSAR data-set from the Caprivi region of Namibia during flooding in 2009 and 2010 confirm the effectiveness of integrating hierarchical as well as spatio-temporal context into the labeling process, and of adapting the models to irregular graph structures. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
11. Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features.
- Author
-
Tsyganskaya, Viktoriya, Martinis, Sandro, and Marzahn, Philip
- Subjects
TIME series analysis ,VEGETATION classification ,SYNTHETIC aperture radar ,CRISIS management ,WEATHER ,FLOODS - Abstract
Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer's/User's accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
12. Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data.
- Author
-
Tsyganskaya, Viktoriya, Martinis, Sandro, Marzahn, Philip, and Ludwig, Ralf
- Subjects
- *
GROUND vegetation cover , *SURFACE of the earth , *LANDFORMS , *LAND cover - Abstract
The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.