While operational data acquisition for the TanDEM-X DEM took place from 2011 to early 2015, the availability of the final DEM version on global scale is expected for autumn 2016. A next milestone, however, is the data acquisition for the upcoming so-called high-resolution DEM (HDEM) product which is being performed since spring 2015. The TanDEM-X HDEM will be available for selected test sites on local scale. The production process will begin in 2017. This paper gives an overview of the HDEM acquisition and processing concept and its product characteristics. Moreover, a first HDEM showcase data set has been produced verifying the expected reduction of the relative height error compared to the DEM.

The TanDEM-X mission (TDM) is a spaceborne radar interferometer which delivers a global digital surface model (DSM) with an unprecedented spatial resolution. This allows resolving objects above ground such as buildings. Extracting and characterizing those objects in an automated manner represents a challenging problem but opens simultaneously a broad range of large-area applications. In this paper, we discuss and evaluate the suitability of morphological filters (MFs) for the derivation of normalized DSMs from the TDM in complex urban environments and introduce a novel region-growing-based progressive MF procedure. This approach is jointly proposed and can be combined with a postclassification processing scheme to specifically allow for a viable reconstruction of urban morphology in a challenging terrain. The filter approach comprises a multistep procedure using concepts of morphological image filtering, region growing, and interpolation techniques. Therefore, it extends the idea of progressive MFs. The latter aim to identify nonground pixels in the DSM by gradually increasing the size of a structuring element and applying iteratively an elevation difference threshold. After the identification of initial nonground pixels, here, potential nonground pixels are identified within each iteration, and their similarity with respect to neighboring nonground pixels is assessed. Pixels are finally labeled as nonground if a constraint is fulfilled. The postclassification processing scheme adapts techniques of object-based image analyses to further refine regions of classified nonground pixels. Digital terrain models are subsequently generated by interpolating between identified ground pixels. Experimental results are obtained for settlement areas that cover large parts of the cities of Izmir (Turkey) and Wuppertal (Germany). They confirm the capability of the proposed approaches for a reduction of omission errors compared to basic MF-based methods when classifying ground pixels, which is favorable in a mountainous terrain with steep slopes.

With begin of 2013 DEM production started with the generation of the Intermediate TanDEM-X DEM (IDEM) for selected science and commercial test sites. This paper gives an overview of the Intermediate DEM production that took place from end of 2012 until July 2013. It describes the DEM production process and the lessons learnt by the IDEM phase. Since autumn 2013 the final DEM generation has begun in flat areas.

In September 2013 the production of TanDEM-X digital elevation model (DEM) started. As the data acquisition for difficult terrain lasted until April 2014, final DEM production started for flat to moderate terrain regions where two final coverages surfice. This paper focuses on a first validation of moderate terrain to prove the absolute height accuracy. In a detailed comparison three DEM tiles from different continents are chosen to validate the TanDEM-X DEM by computing differences to GPS tracks, ICESat validation points, and SRTM. On a global scale all TanDEM-X DEMs produced so far are compared with ICESat and GPS tracks. Both validations presented here for the first time indicate that the absolute height error for moderate terrain for TanDEM-X is below 2m and therefore much better than the specified 10m/LE90.

The German SAR interferometry mission TanDEM-X performed on two TerraSAR-X satellites flying in close formation will provide a global Digital Elevation Model (DEM). A by-product is so-called the Water Indication Mask (WAM). The purpose of this supplementary information layer is to support the DEM editing process. Water surfaces usually show lower coherence in an interferometric data set due to temporal de-correlation and low backscattering. Consequently the corresponding elevation values derived from the interferogram are random and produce a virtual relief. This paper introduces the operational water body detection workflow that synergistically evaluates amplitude and coherence information. The presented results of two test sites reveal that the methodology is globally applicable, classifications are highly accurate and the algorithm is appropriate for operational image processing. The water body detection consists of two steps: the Water Body Detection (WBD) derived of one single DEM scene and the mosaicking of multiple WBD to a single Water Indication Mask (WAM). The fusion strategy for the final TanDEM-X WAM considers all WBD acquired at different times in two global coverages and bases on a fusion by union containing the results of the amplitude and the coherence.

For the global TanDEM-X DEM the whole world will be acquired by at least two coverages. Thereby on the one hand phase unwrapping errors are reduced by applying the dual-baseline method and on the other hand a low noise level is ensured even for difficult areas like forests and steep terrain. During DEM mosaicking, the single interferometric DEMs are merged together. This paper focuses on the combination of heights in overlapping areas with significant height differences. The challenge here is to choose the most reliable height value. The improvement applying this strategy in contrast to simple averaging and the general benefit of using more than one acquisition is shown by means of some example mosaics.

The main product of the TanDEM-X mission is a global interferometric digital elevation model (DEM) that is finally calibrated due to residual systematic offsets and tilts. For the final DEM product single acquisitions (so-called data takes) are calibrated and merged to tiles of a size of 1°×1°. Finally the globe will mostly be covered with two acquisitions. The quality of the calibration of the data takes and also the accuracy of the final DEM highly depends on the terrain and the vegetation. Therefore, three different test areas where we have to deal with good, medium and difficult terrain conditions will be presented here.

The main goal of the TanDEM-X mission is the production of a global Digital Elevation Model (DEM). A byproduct is the so-called Water Indication Mask (WAM). The purpose of this supplementary information layer is to support the DEM editing process where the DEM is noisy. The WAM is derived from the SAR amplitude and the single-pass coherence. In this paper, the methodology of the water body detection is briefly explained and the results of four test sites covering different climatic regions are evaluated. The different characteristics of the WAM using amplitude and coherence image are described and their respective pros and cons are discussed.

The main product of the TanDEM-X mission is an interferometric DEM product that is finally calibrated due to residual systematic offsets and tilts and where different, generally two coverages, are mosaicked. Above this, a water mask is provided to support later editing of rough water areas. In this presentation the commissioning phase work to set the DEM production chain into operation is described and the first commissioning phase products are shown.

Additionally to the main product of the TanDEM-X mission - the global Digital Elevation Model (DEM) - a global water body mask will be produced. The main goal of this water mask is to deliver an information layer to support a subsequent DEM editing process. It is derived from the SAR amplitude and the single pass coherence. In this paper, the concept of the global water body detection is explained and a first evaluation of the single coverage water body detection is presented.

The global process of urbanization is associated with various ecological, social and economic changes in both the built-up area and the adjacent natural or cultivated landscape. To manage the effects and impacts of this development, effective urban and regional planning requires accurate and up to date information on the urban dynamics. This paper introduces a methodology to automatically detect human settlements and then further characterize the identified built up areas in terms of the building density based on VHR SAR data. The SAR imagery is acquired by the German satellite system TerraSAR-X. Regarding the delineation of the built-up area in the region of Munich we achieved an overall accuracy of 94 % and a Kappa of 0.86. The estimation of building density showed a coefficient of determination (r2) of up to 0.74. The mean absolute error of the modeled building densities was 5%.

The aimed accuracies for the final TanDEM-X DEM of 10m absolute and 2m relative height error will be ensured by calibration data. One crucial data set for the relative accuracy is tie-points that connect adjacent DEM acquisitions in the approximately 4km-overlap-area with each other. In this paper an improved concept for tie-point candidates is presented that is based on averaging a larger region instead of comparing single points. This concept should be more robust against noise. It is validated by applying the DEM calibration on a simulated test area, as real TanDEM-X data was not yet available. Also, the DEM calibration will be validated for the first time on a larger “real” test site by applying the TanDEM-X processing scenario.

The current process of megacity development and urban sprawl are unique in human history. More and more so-called megacities with more than 10 million inhabitants are evolving throughout the world. The presented study is focusing on the earthquake-prone megacity Istanbul officially counting 12 million inhabitants in 2007. During the past decades, the megacity has undergone an enormous suburbanization into its outskirts. Recent urban developments, however, seem to indicate changing housing trends respectively types of urbanization in Istanbul. In our study we focus on a multi-temporal and multisensoral analysis using Landsat and TerraSAR-X data. By implementing an object-oriented classification approach settlement masks for 1975, 1987, 2000, and 2008 have been created. Furthermore, post-classification change detection is displaying medium and large scale urban developments of the megacity for the past decades. The results are conforming to current social studies focusing on urbanity and lifestyle: Istanbul is facing new types and factors of urban development The study demonstrates both the synergistic usage of multi-temporal and multi-sensoral remotely sensed data. Additionally, the synergistic potential of remote sensing and applied urban studies to work out useful information for urban planners is presented.

The current process of megacity development and urban sprawl are unique in human history. More and more so-called megacities with more than 10 million inhabitants are evolving throughout the world. The presented study is focusing on the earthquake-prone megacity Istanbul officially counting 12 million inhabitants in 2007. During the past decades, the megacity has undergone an enormous suburbanization into its outskirts. Recent urban developments, however, seem to indicate changing housing trends respectively types of urbanization in Istanbul. In our study we focus on a multi-temporal and multisensoral analysis using Landsat and TerraSAR-X data. By implementing an object-oriented classification approach settlement masks for 1975, 1987, 2000, and 2008 have been created. Furthermore, post-classification change detection is displaying medium and large scale urban developments of the megacity for the past decades. The results are conforming to current social studies focusing on urbanity and lifestyle: Istanbul is facing new types and factors of urban development. The study demonstrates both the synergistic usage of multi-temporal and multi-sensoral remotely sensed data. Additionally, the synergistic potential of remote sensing and applied urban studies to work out useful information for urban planners is presented.

Urbanization is arguably the most dramatic form of highly irreversible land transformation. While urbanization is a worldwide phenomenon, it is exceptionally dynamic in India, where unprecedented urban growth rates have occurred over the last 30 years. In this uncontrolled explosive situation city planning lacks of data and information to measure, monitor, understand urban sprawl processes. The analysis of such changes has become an important use of multitemporal remote sensing data. Using a time-series of Landsat data to classify the urban footprints since the 1970s enables detection of temporal and spatial urban sprawl, redensification and urban development in the explosively growing large urban agglomerations of the mega cities Mumbai, Delhi and Kolkata in India. Combining gradient analysis with landscape metrics the spatiotemporal pattern of urbanization are quantified. Spatial parameters are the absolute areal growth, urbanization rates, built-up densities, landscape shape index, edge density, patch density, or largest patch index. The study aims to detect analogies and differences for spatial growth in Indian mega cities, cities in the same cultural area at about the same development stage regarding absolute population. The results paint a characteristic picture of spatial pattern gradients and landscape metrics of the three Indian mega cities.

One way to scale up clustering algorithms is to squash the data by some intelligent compression technique and cluster only the compressed data records. Such compressed data records can e.g. be produced by the BIRCH algorithm. Typically they consist of the sufficient statistics of the form (N, X, X2) where N is the number of points, X is the (vector-)sum, and X2 is the square sum of the points. They can be used directly to speed up k-means type of clustering algorithms, but it is not obvious how to use them in a hierarchical clustering algorithm. Applying a hierarchical clustering algorithm e.g. to the centers of compressed subclusters produces a very weak result. The reason is that hierarchical clustering algorithms are based on the distances between data points and that the interpretaion of the result relies heavily on a correct graphical representation of these distances. In this paper, we introduce a method by which the sufficient statistics (N, X, X2) of sub-clusters can be utilized in the hierarchical clustering method OPTICS. We show how to generate appropriate distance information about compressed data points, and how to adapt the graphical representation of the clustering result. A performance evaluation using OPTICS in combination with BIRCH demonstrates that our approach is extremely efficient (speed-up factors up to 1700) and produces high quality results.