Analysis of DEM combination methods using high resolution optical stereo imagery and interferometric SAR data

Digital elevation models (DEM) from satellite data are generated mainly from two types of datasets using completely different methods: photogrammetry for optical stereo images (e.g. SPOT5, CARTOSAT) and interferometry for Synthetic Aperture Radar data (InSAR, e.g. ERS-Tandem, SRTM). Both generation methods show advantages and disadvantages but have similar accuracy values in comparison to a reference DEM. The paper aims at showing the potential for combined usage of several DEM (derived with different sensors and methods) to provide a “gap-less” DEM and improve the overall accuracy. Some results are given for three combination methods: DEM fusion utilizing height error maps for each DEM; DEM integration, where single point information from another DEM is inserted during the triangulation process; and the delta surface fill method. The quality of the DEM derived from one source and of the combined DEM depends on the steepness of the terrain and on the land cover type. For flat terrain or moderate hilly landscapes, a height accuracy in the order of 5 meters (RMSE) or better can be achieved for the mentioned sensors. Two test areas are chosen, where many different data sets are available and much knowledge exists from previous studies. The first test area is a region in the south-eastern part of Bavaria comprising a mostly hilly, post-glacial landscape. The second test area is located in Catalonia, Spain including also a mostly hilly terrain with some steep slopes. The received DEM are compared qualitatively and quantitatively to the reference DEM with superior quality by looking at profiles and statistics. The results show an improvement of the combined DEM that can be quantitatively measured. Although overall statistics for larger regions show only a slight improvement, local errors and blunders are reduced significantly and the overall accuracy of the combined DEM is higher

Detailed Damage Assessment after the Haiti Earthquake

In the post crisis phase a much more detailed analysis can be done with higher accuracy and less pressure of time compared to the general situation assessment of the rapid mapping process directly after the crisis. In this investigation the analysis is concentrated on the urban area of the capital town Port-au-Prince. In order to develop a service for detailed damage assessment, methods of (semi-)automatic change detection are used and compared, since up to now, damage information was mainly derived by visual interpretation. Any improvement in terms of accuracy and speed of analysis is of relevance to users in this context. The results of the different change detection algorithms achieved using the Haiti datasets are compared to each other and also to the database of the Haiti Action Plan for Reconstruction and Development (PDNA). The results are very promising although further improvements have to be made

Comparison of DEM Generation and Combination Methods Using High Resolution Optical Stereo Imagery and Interferometric SAR Data

Digital elevation models (DEM) from satellite data are generated mainly from two types of datasets using completely different methods: photogrammetry for optical stereo images (e.g. SPOT5, IKONOS) and interferometry for Synthetic Aperture Radar data (InSAR, e.g. ERS-Tandem, SRTM). Both generation methods show advantages and disadvantages but have similar accuracy values in comparison to a reference DEM. The paper aims at showing the potential for combined usage of several DSM (derived with different sensors and methods) to improve the overall accuracy. Some results are given for DEM fusion utilizing height error maps for each DSM and for DEM integration, where single point information from another DSM is inserted during the triangulation process. The quality of the DSM derived from one source and of the combined DSM depends on the steepness of the terrain and on the land cover type. For flat terrain or moderate hilly landscapes, a height accuracy in the order of 5 meters or better can be achieved for the mentioned sensors. Two test areas are chosen, where many different data sets are available and much knowledge exists from previous studies. The first test area is a region in the south-eastern part of Bavaria comprising a mostly hilly, post-glacial landscape including lakes and also mountains of the German Alps. The second test area is located in Catalonia, Spain, and includes the city of Barcelona as well as a mostly hilly terrain with some steep slopes and additionally the Mediterranean coast. The received DSM are compared qualitatively and quantitatively to the reference DEM with superior quality by looking at profiles and sub-area statistics. The results show that an improvement of the fused DSM and the integrated DSM can be quantitatively measured. Although the overall statistics for a larger region does show only a slight improvement, local errors can be reduced significantly so that the overall accuracy of the combined DSM is higher

Change Detection for Reconstruction Monitoring based on Very High Resolution Optical Data

Change detection techniques are widely used in environmental monitoring, however, the issue of best suitability of change detection techniques for a specific application, even study area is still unanswered. In case of reconstruction monitoring, difference-based change detection methods are compared and evaluated in detecting changes on the study area Banda Aceh by using very high resolution optical data in this paper. They are classical image differencing, iteratively reweighted multivariate alteration detection (IR-MAD) and IR-MAD incorporating some textural features. The experimental results show that IR-MAD method has the best performance. Compared with manually acquired reference data, the change detection map produced by IR-MAD method is satisfying and promising

Radar Remote Sensing of Ocean Waves - Global Mapping of Mean and Peak Wave Parameters Extracted from SAR Cross Spectra

Satellite remote sensing offers different methods for acquiring information on the open ocean and coastal regions. The synthetic aperture radar (SAR) is the only instrument capable of providing high-resolution 2D sea surface spectra on a global and continuous scale. Besides spectral sea state information, SAR image data (imagettes) of the sea surface offer plenty of additional information, especially about atmospheric effects. The aim of the present thesis is to investigate the performance of the ocean wave model WAM in comparison to marine parameters extracted from complex ERS-2 SAR data, as well as to investigate severe weather situations, e.g., hurricanes, with SAR imagettes and computed spectra. For this purpose, observed SAR cross spectra and modelled WAM ocean wave spectra are analysed. Typical sea state parameters like significant wave height and wavelength are retrieved from the spectra and evaluated. Tools generating global statistics and maps to analyse and present the information contained in the spectra are developed and applied to the collocated data sets. A new method to detect inconsistencies between observation and model is developed by forward mapping of modelled wave spectra into cross spectra for an independent comparison. Possible explanations for the deviations are discussed

Towards Automated DEM Generation from High Resolution Stereo Satellite Images

High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). In this paper, a system for highly automated DSM and orthoimage generation based on CARTOSAT-1 imagery is presented. The proposed system processes photometrically corrected level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The RPC are derived from orbit and attitude information and have a much lower accuracy than the ground resolution of approximately 2.5 m. Ground control points are used to estimate affine RPC correction. Accurate GCP are not always available, especially for remote areas and large scale reconstruction. In this paper, GCP are automatically derived from lower resolution reference images (Landsat ETM+ Geocover and SRTM DSM). It is worthwhile to note that SRTM has a much higher lateral accuracy than the Landsat ETM+ mosaic, which limits the accuracy of both DSM and orthorectified images. Thus, affine RPC correction parameters are estimated by aligning a preliminary DSM to the SRTM DSM, resulting in significantly improved geolocation of both DSM and orthoimages. Robust stereo matching and outlier removal techniques and prior information such as cloud masks are used during this process. DSM with a grid spacing of 10 m are generated for 9 CARTOSAT-1 scenes in Catalonia. Checks against independent ground truth indicate a lateral error of 3-4 meters and a height accuracy of 4-5 meters. Independently processed scenes align at subpixel level and are well suited for mosaicing

Development of a new spectral library classifier for airborne hyperspectral images on heterogeneous environments

The classification of hyperspectral images on heterogeneous environments without prior knowledge about the study area is a challenging task. Finding potential pure spectral signatures or endmembers (EM) of the various surface materials within an image is essential for obtaining accurate classification results. Automated endmember selection techniques, in many cases, return an unlabelled result without a relationship to a known material. This study demonstrates the potential of an automated spectral classification approach for hyperspectral imagery by using a comprehensive spectral library including a generalized class structure without the use of prior knowledge of the given scene. The classifier works by comparing every unknown image pixel to all labelled known spectra in the spectral library using a mixed measure similarity analysis of the spectral information divergence SID (Chang, 2000), the spectral angle mapper SAM (Kruse et. al., 1993) and the tangent trigonometric function (Du et. al., 2004). These similarity measures are the main criteria used to assign the class membership to a given pixel. In addition, a statistical analysis of the best ten scores identifies the statistical dominant material class from the similarity analysis. This statistical approach allows a pixel-related estimation of the classification reliability. The spectral library comparison classifier (SLC-Classifier) enables the classification of hyperspectral images on heterogeneous environments to be as complete as possible (depends on the input spectral library) with results containing both labelled potential pure spectra and spectra with low similarity agreement. Pixels with low similarity agreement are mixed pixels and pixels related to materials without good representative spectra in the comprehensive spectral library. We demonstrate that this classifier is suitable for the identification of surface materials using hyperspectral images were detailed knowledge about the environments does not exist

Remote sensing of morphodynamics

Morphological changes in coastal areas, especially in the river estuaries, are of high interest, e.g. for maping, navigation, flood and landscape protection. Caused by the currents of both the tidal and river water masses morphodynamics in river estuaries are higher than in ordinary coastal region. Remote sensing techniques are frequently used to monitor these changes. Various datasets, techniques and applications will be presented here. These range from synthetic aperture radar (SAR) and interferometric SAR (InSAR) to optical data, from airborne to spaceborne data, from coastal monitoring to digital elevation model (DEM) generation

Comparison of orthorectification methods suitable for rapid mapping using direct georeferencing and RPC for optical satellite data

One of the first essential steps in response to a crisis event is to provide current, precise, and rapid information about the extent of the event, the affected population and infrastructure (e.g., information for relief teams). Experiences in crisis mapping show, that remote sensing data are an important information source to get an overview of the situation. For an efficient use of geo-information before or during a crisis of any kind, all information should be orthorectified. Satellite data are delivered normally geocoded with varying accuracy. However, today orthorectification without ground control points (GCP) using only ephemeris and attitude data measured on board the satellite provide an absolute accuracy of about 20 m to 1 km (depending on the satellite). For the intended applications like change detection this accuracy is not sufficient. For this purpose, accuracy in the sub-pixel range is necessary requiring a precise orthorectification. Different methods deployed in different software packages are analyzed and compared in this paper. They are chosen depending on the available metadata. To get an objective comparison of the different orthorectification methods, their accuracy is determined for several test datasets in this study. Additionally to the comparison of the accuracy, further criteria are analyzed like the potential of the algorithm for optimization and automation and the computing time needed, since the timely supply of information is also one of the most important requirements in crisis respond mapping. Results using the commercially available software packages Erdas Imagine, PCI Geomatics and ENVI are compared to products of the in-house developed software XDibias of the German Aerospace Center (DLR). All programs result in high accuracy. Differences are experienced in the computing times and userfriendlyness. While the commercial programs are very user friendly, knowledge about the details of the in-house developed software are very helpful and the automation potential of it has already been proven