Comparing four operational SAR-based water and flood detection approaches.
In recent years, the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) has gained a lot of experience in water surface extraction from synthetic aperture radar (SAR) data for various application domains. In this context, four approaches have been developed, which jointly form the so-called DFD Water Suite: The Water Mask Processor (WaMaPro) is based on a simple and high-performance algorithm that processes multi-sensor SAR data in order to provide decision-makers with information about the location of water surfaces. The Rapid Mapping of Flooding (RaMaFlood) has been developed for flood extent mapping using an interactive object-based classification algorithm. The TerraSAR-X Flood Service (TFS) is used for rapid mapping activities and provides satellite-derived information about the extent of floods in order to support management authorities and decision-makers. It is based on a fully automated processing chain. The last approach is the TanDEM-X Water Indication Mask processor (TDX WAM). It is part of the processing chain for the generation of the seamless, accurate, and high-resolution global digital elevation model (DEM) produced based on data of the TanDEM-X mission. Its purpose is to support the subsequent DEM editing process by the generation of a global reference water mask. In this study, the design of the four approaches and their methodological backgrounds are explained in detail, while simultaneously elaborating on the preferred application domains for the different algorithms. The advantages and disadvantages of the four approaches are identified by qualitatively as well as quantitatively evaluating the water masks derived from data of the TanDEM-X mission for five test sites located in Vietnam, China, Germany, Mali, and the Netherlands. [ABSTRACT FROM AUTHOR]/nCopyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Application domainInput remotesensing dataAuxiliary dataWater probability mask generatedWeb interface
Comparison of:œ Water Mask Processor (WaMaPro); œ Rapid Mapping of Flooding (RaMaFlood); œ TSX Flood Service (TFS); œ TanDEM-X Water Indication Mask processor (TDX WAM).
the data basis for this study is TanDEM-X co-registered single-look slantrange complex (CoSSC) data, which are acquired in X-band with a signal wavelength, ë, of 3.1 cm.Five test areas have been selected to compare the four proposed approaches WaMaPro, RaMaFlood, TFS, and TDX WAM based on subsets of StripMap data of the TanDEM-X mission. The test areas are distributed over three continents and are located in Vietnam, the Netherlands, Germany, Mali, and China (Figure 1), covering areas of 538.4, 324.4, 452.9, 284.2, and 120.1 km2, respectively
In general, the performance of the four approaches is very satisfying: out of the 20 water masks 17 reach OAs of >90% and 11 OAs of >95%. Also, both the UAs and the PAs give reasonable results for nearly all approaches, with most values clearly above 85.0%The four approaches give similar classification results of the PAs, UAs, and OAs in areas where open water surfaces are relatively smooth and the occurrence of water look alike areas is low, i.e. in the test areas of Vietnam and China. In areas of low contrast between open water surfaces and the surrounding non-water areas, the classification result strongly depends on the selected threshold value. In the test area of Mali, many sand-covered areas exist that exhibit backscatter levels nearly as low as water areasIn cases of occurring water look alike areas, TFS and RaMaFlood, which both use auxiliary data derived from digital elevation models, lead to the best classification results.
In this study, the design of the four approaches and their methodological backgrounds are explained in detail, while simultaneously elaborating on the preferred application domains for the different algorithms. The advantages and disadvantages of the four approaches are identified by qualitatively as well as quantitatively evaluating the water masks derived from data of the TanDEM-X mission for five test sites located in Vietnam, China, Germany, Mali, and the Netherlands.
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