18 results on '"Martinis, Sandro"'
Search Results
2. Global Flood Monitoring Webinar 2022: Products Outline
- Author
-
Krullikowski, Christian, Chow, Candace Wing-Yuen, Wieland, Marc, and Martinis, Sandro
- Subjects
Disaster ,Sentinel-1 ,Remote sensing ,Floods - Published
- 2022
3. Artificial Intelligence for flood analysis: first results from the AI4Flood project
- Author
-
Garg, Shagun, Xian, Tianqi, Motagh, Mahdi, Martinis, Sandro, Plank, Simon Manuel, and Wieland, Marc
- Subjects
Machine Learning ,Floods ,SAR - Published
- 2021
4. A temporal-ensembling active self-learning CNN framework for urban flood mapping by means of multi-temporal SAR intensity/coherence
- Author
-
Li, Yu, Martinis, Sandro, and Wieland, Marc
- Subjects
urban areas ,Floods ,CNN ,TerraSAR-X - Published
- 2019
5. An automatic system for near-real time flood extent and duration mapping based on Sentinel-1, Sentinel-2, and TerraSAR-X data
- Author
-
Martinis, Sandro, Bettinger, Michaela, Wieland, Marc, Schlaffer, Stefan, Böhnke, Christian, Shakya, Hausala, Nolde, Michael, Plank, Simon Manuel, Hess, Ulrich, Sharma, Vaibhav, Jangle, Nihar, Milosch, Oliver, and Strunz, Günter
- Subjects
extent ,Duration ,Sentinel-1 ,Sentinel-2 ,Floods ,TerraSAR-X - Published
- 2019
6. ASAPTERRA - Advancing SAR and Optical Methods for Rapid Mapping
- Author
-
Martinis, Sandro, Clandillon, Stephen, Plank, Simon, Twele, André, Huber, Claire, Caspard, Mathilde, Maxant, Jérôme, Cao, Wenxi, Haouet, Sadri, and Fuchs, Eva-Maria
- Subjects
landslides ,Rapid mapping ,natural hazards ,floods ,ALOS-2/PALSAR-2 ,fires ,Sentinel-1 ,Pléiades ,Georisiken und zivile Sicherheit ,Sentinel-2 ,TerraSAR-X - Abstract
Optical and radar satellite remote sensing have proven to provide essential crisis information in case of natural disasters, humanitarian relief activities and civil security issues in a growing number of cases through mechanisms such as the Copernicus Emergency Management Service (EMS) of the European Commission or the International Charter ‘Space and Major Disasters’. The aforementioned programs and initiatives make use of satellite-based rapid mapping services aimed at delivering reliable and accurate crisis information after natural hazards. Although these services are increasingly operational, they need to be continuously updated and improved through research and development (R&D) activities. The principal objective of ASAPTERRA (Advancing SAR and Optical Methods for Rapid Mapping), the ESA-funded R&D project being described here, is to improve, automate and, hence, speed-up geo-information extraction procedures in the context of natural hazards response. This is performed through the development, implementation, testing and validation of novel image processing methods using optical and Synthetic Aperture Radar (SAR) data. The methods are mainly developed based on data of the German radar satellites TerraSAR-X and TanDEM-X, the French satellite missions Pléiades-1A/1B as well as the ESA missions Sentinel-1/2 with the aim to better characterize the potential and limitations of these sensors and their synergy. The resulting algorithms and techniques are evaluated in real case applications during rapid mapping activities. The project is focussed on three types of natural hazards: floods, landslides and fires.
- Published
- 2017
7. Automated flood mapping and monitoring using Sentinel-1 data
- Author
-
Twele, Andre, Martinis, Sandro, Cao, Wenxi, and Plank, Simon
- Subjects
NRT processing ,Classification ,Floods ,SAR - Published
- 2016
8. Four operational SAR-based water and flood detection approaches: a comparison
- Author
-
Martinis, Sandro, Wendleder, Anna, Künzer, Claudia, Huth, Juliane, Twele, André, Roth, Achim, and Dech, Stefan
- Subjects
Mapping ,Inland Waters ,NRT processing ,Classification ,Floods - Published
- 2016
9. An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data.
- Author
-
Li, Yu, Martinis, Sandro, Plank, Simon, and Ludwig, Ralf
- Subjects
- *
FLOODS , *SYNTHETIC aperture radar , *ECOLOGICAL mapping - Abstract
Highlights • We present an automatic change detection processing chain for rapid flood mapping. • The reference image is selected automatically by Jensen-Shannon (JS) divergence. • Saliency-guided generalized Gaussian mixture model combined with fully-connected CRF. • It is capable of handling highly imbalanced data. • Fine structures in flooded area are preserved when the noise is removed. Abstract In this paper, a two-step automatic change detection chain for rapid flood mapping based on Sentinel-1 Synthetic Aperture Radar (SAR) data is presented. First, a reference image is selected from a set of potential image candidates via a Jensen-Shannon (JS) divergence-based index. Second, saliency detection is applied on log-ratio data to derive the prior probabilities of changed and unchanged classes for initializing the following expectation-maximization (EM) based generalized Gaussian mixture model (GGMM). The saliency-guided GGMM is capable of capturing the primary pixel-based change information and handling highly imbalanced datasets. A fully-connected conditional random field (FCRF) model, which takes long-range pairwise potential connections into account, is integrated to remove the ambiguities of the saliency-guided GGMM and to achieve the final change map. The whole process chain is automatic with an efficient computation. The proposed approach was validated on flood events at the Evros River, Greece and the Wharfe River and Ouse River in York, United Kingdom. Kappa coefficients (k) of 0.9238 and 0.8682 were obtained respectively. The sensitivity analysis underlines the robustness of the proposed approach for rapid flood mapping. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
10. Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery.
- Author
-
Plank, Simon, Jüssi, Martin, Martinis, Sandro, and Twele, André
- Subjects
POLARIMETRIC remote sensing ,VEGETATION mapping ,FLOODS ,SYNTHETIC aperture radar ,DECOMPOSITION method - Abstract
This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
11. Sentinel-1-based flood mapping: a fully automated processing chain.
- Author
-
Twele, André, Cao, Wenxi, Plank, Simon, and Martinis, Sandro
- Subjects
FLOODS ,MATHEMATICAL mappings ,SATELLITE-based remote sensing ,ONLINE monitoring systems ,MATHEMATICAL models - Abstract
This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-real-time (NRT). Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows deriving time-critical disaster information in less than 45 min after a new data set is available on the Sentinel Data Hub of the European Space Agency (ESA). Due to the systematic acquisition strategy and high repetition rate of Sentinel-1, the processing chain can be set up as a web-based service that regularly informs users about the current flood conditions in a given area of interest. The thematic accuracy of the thematic processor has been assessed for two test sites of a flood situation at the border between Greece and Turkey with encouraging overall accuracies between 94.0% and 96.1% and Cohen’s kappa coefficients (κ) ranging from 0.879 to 0.910. The accuracy assessment, which was performed separately for the standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that under calm wind conditions, slightly higher thematic accuracies can be achieved by using VV instead of VH polarization data. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
12. Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping.
- Author
-
Martinis, Sandro, Groth, Sandro, Wieland, Marc, Knopp, Lisa, and Rättich, Michaela
- Subjects
- *
FLOOD warning systems , *WATER pipelines , *EMERGENCY management , *SEASONS , *FLOODS , *BODIES of water , *SPATIAL resolution - Abstract
Satellite-based flood mapping has become an important part of disaster response. In order to accurately distinguish flood inundation from normally present conditions, up-to-date, high-resolution information on the seasonal water cover is crucial. This information is usually neglected in disaster management, which may result in a non-reliable representation of the flood extent, mainly in regions with highly dynamic hydrological conditions. In this study, we present a fully automated method to generate a global reference water product specifically designed for the use in global flood mapping applications based on high resolution Earth Observation data. The proposed methodology combines existing processing pipelines for flood detection based on Sentinel-1/2 data and aggregates permanent as well as seasonal water masks over an adjustable reference time period. The water masks are primarily based on the analysis of Sentinel-2 data and are complemented by Sentinel-1-based information in optical data scarce regions. First results are demonstrated in five selected study areas (Australia, Germany, India, Mozambique, and Sudan), distributed across different climate zones and are systematically compared with external products. Further, the proposed product is exemplary applied to three real flood events in order to evaluate the impact of the used reference water mask on the derived flood extent. Results show, that it is possible to generate a consistent reference water product at 10–20 m spatial resolution, that is more suitable for the use in rapid disaster response than previous masks. The proposed multi-sensor approach is capable of producing reasonable results, even if only few or no information from optical data is available. Further it becomes clear, that the consideration of seasonality of water bodies, especially in regions with highly dynamic hydrological and climatic conditions, reduces potential over-estimation of the inundation extent and gives a more reliable picture on flood-affected areas. • Automatic large-scale water segementation based on Sentinel-1 and Sentinel-2 data. • Fusing Sentinel-1 and Sentinel-2 time-series data for global water mask generation. • Computation of a permanent and seasonal reference water product at 10 m resolution. • Extensive comparison to external permanent and seasonal reference water products. • The use of seasonal reference water masks improves flood mapping. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
13. A fully automated TerraSAR-X based flood service.
- Author
-
Martinis, Sandro, Kersten, Jens, and Twele, André
- Subjects
- *
SYNTHETIC aperture radar , *FLOODS , *REAL-time computing , *HIGH resolution imaging , *ELECTRONIC data processing - Abstract
In this paper, a fully automated processing chain for near real-time flood detection using high resolution TerraSAR-X Synthetic Aperture Radar (SAR) data is presented. 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 satellite data delivery. The dissemination of flood maps resulting from this service is performed through an online service which can be activated on-demand for emergency response purposes (i.e., when a flood situation evolves). The classification methodology is based on previous work of the authors but was substantially refined and extended for robustness and transferability to guarantee high classification accuracy under different environmental conditions and sensor configurations. With respect to accuracy and computational effort, experiments performed on a data set of 175 different TerraSAR-X scenes acquired during flooding all over the world with different sensor configurations confirm the robustness and effectiveness of the proposed flood mapping service. These promising results have been further confirmed by means of an in-depth validation performed for three study sites in Germany, Thailand, and Albania/Montenegro. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
14. 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
15. Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data.
- Author
-
Rättich, Michaela, Martinis, Sandro, and Wieland, Marc
- Subjects
- *
WATER supply , *REMOTE-sensing images , *FLOOD risk , *IMAGE processing , *IMAGE analysis , *WATER , *AUTOMATIC dependent surveillance-broadcast , *FLOODS - Abstract
Flood duration is a crucial parameter for disaster impact assessment as it can directly influence the degree of economic losses and damage to structures. It also provides an indication of the spatio-temporal persistence and the evolution of inundation events. Thus, it helps gain a better understanding of hydrological conditions and surface water availability and provides valuable insights for land-use planning. The objective of this work is to develop an automatic procedure to estimate flood duration and the uncertainty associated with the use of multi-temporal flood extent masks upon which the procedure is based. To ensure sufficiently high observation frequencies, data from multiple satellites, namely Sentinel-1, Sentinel-2, Landsat-8 and TerraSAR-X, are analyzed. Satellite image processing and analysis is carried out in near real-time with an integrated system of dedicated processing chains for the delineation of flood extents from the range of aforementioned sensors. The skill of the proposed method to support satellite-based emergency mapping activities is demonstrated on two cases, namely the 2019 flood in Sofala, Mozambique and the 2017 flood in Bihar, India. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
16. A Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data.
- Author
-
Wieland, Marc and Martinis, Sandro
- Subjects
- *
ARTIFICIAL neural networks , *WATER consumption , *FLOODS , *CLOUDINESS - Abstract
Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. We present a generic processing chain that covers all modules required for operational flood monitoring from multi-spectral satellite data. This includes data search, ingestion and preparation, water segmentation and mapping of flooded areas. Segmentation of the water extent is done by a convolutional neural network that has been trained on a global dataset of Landsat TM, ETM+, OLI and Sentinel-2 images. Clouds, cloud shadows and snow/ice are specifically handled by the network to remove potential biases from downstream analysis. Compared to previous work in this direction, the method does not require atmospheric correction or post-processing and does not rely on ancillary data. Our method achieves an Overall Accuracy (OA) of 0.93, Kappa of 0.87 and Dice coefficient of 0.90. It outperforms a widely used Random Forest classifier and a Normalized Difference Water Index (NDWI) threshold method. We introduce an adaptable reference water mask that is derived by time-series analysis of archive imagery to distinguish flood from permanent water. When tested against manually produced rapid mapping products for three flood disasters (Germany 2013, China 2016 and Peru 2017), the method achieves ≥ 0.92 OA, ≥ 0.86 Kappa and ≥ 0.90 Dice coefficient. Furthermore, we present a flood monitoring application centred on Bihar, India. The processing chain produces very high OA (0.94), Kappa (0.92) and Dice coefficient (0.97) and shows consistent performance throughout a monitoring period of one year that involves 19 Landsat OLI ( μ Kappa = 0.92 and σ Kappa = 0.07 ) and 61 Sentinel-2 images ( μ Kappa = 0.92 , σ Kappa = 0.05 ). Moreover, we show that the mean effective revisit period (considering cloud cover) can be improved significantly by multi-sensor combination (three days with Sentinel-1, Sentinel-2, and Landsat OLI). [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
17. Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion.
- Author
-
Li, Yu, Martinis, Sandro, Wieland, Marc, Schlaffer, Stefan, and Natsuaki, Ryo
- Subjects
- *
SYNTHETIC aperture radar , *AERIAL photographs , *FLOOD risk , *CITIES & towns , *RURAL geography , *FLOODS - Abstract
Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
18. 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
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.