Achim Roth

The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar

For 11 days in February 2000, the Shuttle Radar Topography Mission (SRTM) successfully recorded by interferometric synthetic aperture radar (InSAR) data of the entire land mass of the earth between 60°N and 57°S. The data acquired in C- and X-bands are processed into the first global digital elevation models (DEMs) at 1 arc sec resolution, by NASA-JPL and German aerospace center (DLR), respectively. From the perspective of the SRTM-X system, we give in this paper an overview of the mission and the DEM production, as well as an evaluation of the DEM product quality. Special emphasis is on challenges and peculiarities of the processing that arose from the unique design of the SRTM system, which has been the first single-pass interferometer in space.

Urbanization in India – Spatiotemporal analysis using remote sensing data

Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.

Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission

The German TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission (TDM) collects two global data sets of very high resolution (VHR) synthetic aperture radar (SAR) images between 2011 and 2013. Such imagery provides a unique information source for the identification of built-up areas in a so far unique spatial detail. This letter presents the novel implementation of a fully automated processing system for the delineation of human settlements worldwide based on the SAR data acquired in the context of the TDM. The proposed Urban Footprint Processor (UFP) includes three main processing stages dedicated to: i) the extraction of texture information suitable for highlighting regions characterized by highly structured and heterogeneous built-up areas; ii) the generation of a binary settlement layer (built-up, non-built-up) based on an unsupervised classification scheme accounting for both the original backscattering amplitude and the extracted texture; and iii) a final post-editing and mosaicking phase aimed at providing the final Urban Footprint (UF) product for arbitrary geographical regions. Experimental results assess the high potential of the TDM data and the proposed UFP to provide highly accurate geo-data for an improved global mapping of human settlements.

Improvement of Image Segmentation Accuracy Based on Multiscale Optimization Procedure

This letter proposes an optimization approach that enhances the quality of image segmentation using the software Definiens Developer. The procedure aims at the minimization of over- and undersegmentations in order to attain more accurate segmentation results. The optimization iteratively combines a sequence of multiscale segmentation, feature-based classification, and classification-based object refinement. The developed method has been applied to various remotely sensed data and is compared to the results achieved with the established segmentation procedures provided by the Definiens Developer software. The quantitative assessment of segmentation accuracy based on reference objects is derived from an aerial image, and a high-resolution synthetic aperture radar scene shows an improvement of 20%-40% in object accuracy by applying the proposed procedure.

TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns

Abstract. 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.

Object-based feature extraction using high spatial resolution satellite data of urban areas

Urban morphology is characterized by a complex and variable coexistence of diverse, spatially and spectrally heterogeneous objects. Built-up areas are among the most rapidly changing and expanding elements of the landscape. Thus, remote sensing becomes an essential field for up-to-date and area-wide data acquisition, especially in explosively sprawling cities of developing countries. The urban heterogeneity requires high spatial resolution image data for an accurate geometric differentiation of the small-scale physical features. This study proposes an object-based, multi-level, hierarchical classification framework combining shape, spectral, hierarchical and contextual information for the extraction of urban features. The particular focus is on high class accuracies and stable transferability by fast and easy adjustments on varying urban structures or sensor characteristics. The framework is based on a modular concept following a chronological workflow from a bottom-up segmentation optimization to a hierarchical, fuzzy-based decision fusion top-down classification. The workflow has been developed on IKONOS data for the megacity Istanbul, Turkey. Transferability is tested based on Quickbird data from the various urban structures of the incipient megacity Hyderabad, India. The validation of both land-cover classifications shows an overall accuracy of more than 81 percent.

SAR polarimetric change detection for flooded vegetation

Abstract Due to spatial and temporal variability an effective monitoring system for water resources must consider the use of remote sensing to provide information. Synthetic Aperture Radar (SAR) is useful due to timely data acquisition and sensitivity to surface water and flooded vegetation. The ability to map flooded vegetation is attributed to the double bounce scattering mechanism, often dominant for this target. Dong Ting Lake in China is an ideal site for evaluating SAR data for this application due to annual flooding caused by mountain snow melt causing extensive changes in flooded vegetation. A curvelet-based approach for change detection in SAR imagery works well as it highlights the change and suppresses the speckle noise. This paper addresses the extension of this change detection technique to polarimetric SAR data for monitoring surface water and flooded vegetation. RADARSAT-2 images of Dong Ting Lake demonstrate this curvelet-based change detection technique applied to wetlands although it is applicable to other land covers and for post disaster impact assessment. These tools are important to Digital Earth for map updating and revision.

Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information

The appearance of objects and surfaces in synthetic aperture radar (SAR) images significantly differs from the human perception of the environment. In addition, the quality of SAR data is degraded by speckle noise, superposing the true radiometric and textural information of the radar image. Hence, the interpretation of SAR images is considered to be more challenging compared to the analysis of optical data. However, in this paper, we demonstrate how information on the local development of speckle can be used for the differentiation of basic land cover (LC) types in a single-polarized SAR image. For that purpose, we specify the speckle characteristics of the following LC types: 1) water; 2) open land (farmland, grassland, bare soil); 3) woodland; and 4) urban area by means of an unsupervised analysis of scatter plots and standardized histograms of the local coefficient of variation. Next, we use this information for the implementation of a straightforward preclassification of single-polarized TerraSAR-X stripmap images by combining information on the local speckle behavior and local backscatter intensity. The output is either provided as a discrete classification or as a color composite image whose bands can be interpreted in terms of a fuzzy classification. The results of this paper show that unsupervised speckle analysis in high-resolution SAR images supplies valuable information for a differentiation of the water, open land, woodland, and urban area LC types. While the color composite image supports the visual interpretation of SAR data, the outcome of the fully automated discrete LC classification procedure represents a valuable preclassification image, showing overall accuracies of 77%-86%.

Ensuring globally the TanDEM-X height accuracy: Analysis of the reference data sets ICESat, SRTM and KGPS-tracks

The TanDEM-X mission will derive a global digital elevation model (DEM) with satellite SAR interferometry. Height references play an important role to ensure the required height accuracy of 10m absolute and 2m relative for 90% of the data. In this paper the main height reference data sets ICESat (for DEM calibration), SRTM (for phase unwrapping) and kinematic GPS-Tracks (KGPS — for DEM verification) are analyzed regarding to their accuracy. For the ICESat data a reliable quality measure is developed. For SRTM an improved version adjusted to reliable ICESat data is presented and a concept for collecting and evaluating decimeter-precise kinematic GPS tracks is proposed.

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 tool (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 emergency 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.