TerraSAR-X add-on for digital elevation measurement (TanDEM-X) is a German Earth observation mission collecting a total of two global coverages of very high resolution (VHR) synthetic aperture radar (SAR) X-band data with a spatial resolution of around three meters in the years 2011 and 2012. With these, the TanDEM-X mission (TDM) will provide a unique data set which is complementary to existing global coverages based on medium (MR) or high resolution (HR) optical imagery. The capabilities of the TDM in terms of supporting the analysis and monitoring of global human settlement patterns are explored and demonstrated. The basic methodology for a fully-operational detection and delineation of built-up areas from VHR SAR data is presented along with a description of the resulting geo-information product-the urban footprint (UF) mask-and the operational processing environment for the UF production. Moreover, potential follow-on analyses based on the intermediate products generated in the context of the UF analysis are introduced and discussed. The results of the study indicate the high potential of the TDM with respect to an analysis of urbanization patterns, peri-urbanization, spatio-temporal dynamics of settlement development as well as population estimation, vulnerability assessment and modeling of global change.
This study aims at an area-wide detection of the building structure of settlements from individual, single-polarized
TerraSAR-X (TSX) intensity datasets recorded in stripmap mode. Due to SAR side-looking acquisition, the building-related
information is located in areas which do spatially not exactly correspond with the true location of the buildings.
To perform a supervised classification approach we at first create a mask of areas which are affected by scattering from
the buildings based on reference datasets of the building footprints with their respective height by considering the
viewing geometry of the TSX data. The generated mask is used in the following to randomly extract training samples in
order to determine the relationships between the SAR data and the class membership. For the classification of the areas
carrying the building-related information we utilize a random forest algorithm. As input features for classification we
compare the suitability of the Grey Level Co-occurrence Matrix based textures measures according to Haralick,
Mathematical Morphology and Spatial Autocorrelation texture measures. These features are calculated from TSX data
using a pixel-based multiple-scale moving window approach. For each texture feature set and each moving window
width the relationship to the class membership is modeled on the basis of the extracted training samples. The different
models are used in the following to perform different classification runs of the entire TSX dataset.
With the described approach we achieve overall classification accuracies of up to 78 %. The influence of the
simultaneous usage of input texture features calculated with different window widths on the classification accuracy is of
the same magnitude as the influence of the usage of the different texture feature sets.
Urban areas represent one of the most dynamic regions on earth. To solve the problems which are associated with the
rapid changes in those regions urban and spatial planning relies on up-to-date information about the urban sprawl.
Remote sensing data and in particular Synthetic Aperture Radar (SAR) images can provide valuable information about
the characteristics of urban sprawl with a short repetition rate. In previous studies we could demonstrate the ability to
classify built-up areas with a high accuracy using TerraSAR-X images. This paper focuses on the transfer of this method
to ALOS/PALSAR images. First results of our study show that the method developed for X-band imagery can be
transferred to L-band SAR images. However, the analysis of L-band data still requires some modifications of the
proposed procedure in order to increase the accuracy.
Mega city Mexico City is ranked the third largest urban agglomeration to date around the globe. The large extension as
well as dynamic urban transformation and sprawl processes lead to a lack of up-to-date and area-wide data and
information to measure, monitor, and understand the urban situation. This paper focuses on the capabilities of multisensoral
remotely sensed data to provide a broad range of products derived from one scientific field - remote sensing - to support urban managing and planning. Therefore optical data sets from the Landsat and Quickbird sensors as well as
radar data from the Shuttle Radar Topography Mission (SRTM) and the TerraSAR-X sensor are utilised. Using the
multi-sensoral data sets the analysis are scale-dependent. On the one hand change detection on city level utilising the
derived urban footprints enables to monitor and to assess spatiotemporal urban transformation, areal dimension of urban
sprawl, its direction, and the built-up density distribution over time. On the other hand, structural characteristics of an
urban landscape - the alignment and types of buildings, streets and open spaces - provide insight in the very detailed
physical pattern of urban morphology on higher scale. The results show high accuracies of the derived multi-scale
products. The multi-scale analysis allows quantifying urban processes and thus leading to an assessment and
interpretation of urban trends.
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