Showing papers by "Peter Reinartz published in 2009"

Combining Mutual Information and Scale Invariant Feature Transform for Fast and Robust Multisensor SAR Image Registration

Abstract: The Scale Invariant Feature Transform (SIFT) operator's success for computer vision applications makes it an attractive solution for the intricate feature based SAR image registration problem. For SAR images, SIFT feature matching results into lot of false alarms. To overcome the mentioned problem, we propose to use mutual information (MI) along with the SIFT operator for SAR image registration and matching applications. MI is an established multimodal registration similarity metric and has the capability to quickly estimate rough registration parameters from down-sampled images. The rough image registration parameters obtained using MI can be introduced for conjugate feature selection during the SIFT matching phase. Introduction of MI to the SIFT processing chain not only reduces the number of false alarms drastically but also helps to increase the number of matches as the operator detection and matching thresholds can be relaxed, relying on the available mutual information estimate. Further, the matching consistency of the SIFT matches especially for SAR images with various acquisition differences might not be up to the desired levels. To tackle the observed phenomenon, MI can further be utilized to refine the SIFT matches and to bring the matching consistency within desirable limits. We present our analysis based on multisensor, multitemporal and different view point SAR images acquired over plain and semi urban areas. The proposed registration methodology shows tremendous potential to become a fast and robust alternative for geometric SAR image registration as subpixel registration consistency has been achieved for diverse natured datasets.

Towards automatic near real-time traffic monitoring with an airborne wide angle camera system

Abstract: Large area traffic monitoring with high spatial and temporal resolution is a challenge that cannot be served by today available static infrastructure. Therefore, we present an automatic near real-time traffic monitoring approach using data of an airborne digital camera system with a frame rate of up to 3 fps. By performing direct georeferencing on the obtained aerial images with the use of GPS/IMU data we are able to conduct near real-time traffic data extraction. The traffic processor consists mainly of three steps which are road extraction supported by a priori knowledge of road axes obtained from a road database, vehicle detection by edge extraction, and vehicle tracking based on normalized cross correlation. Traffic data is obtained with a correctness of up to 79% at a completeness of 68%. With this system we are able to perform area-wide traffic monitoring with high actuality independent from any stationed infrastructure which makes the system well suited for deployments on demand in case of disasters and mass events.

A new software/hardware architecture for real time image processing of wide area airborne camera images

Abstract: This paper describes a new software/hardware architecture for processing wide area airborne camera images in real time. The images under consideration are acquired from the 3K-camera system developed at DLR (German Aerospace Center). It consists of three off-the-shelf cameras, each of it delivers 16 Mpixel three times a second. One camera is installed in nadir, whereas the other two cameras are looking in side direction. Main applications of our system are supposed to be automotive traffic monitoring, determining the workload of public road networks during mass events, or obtaining a survey of damages in disaster areas in real time. Altogether, this demands a fast image processing system on the aircraft, because the amount of original high resolution images can not be sent to ground by up-to-date transfer mode systems. The on-board image processing system is distributed over a local network. On each PC several modules are running concurrently. In order to synchronize several processes and to assure access to commonly used data, a new distributed middleware for real time image processing is introduced. Two sophisticated modules one for orthorectification of images and one for traffic monitoring are explained in more detail. The orthorectification and mosaicking is executed on the fast graphics processing unit on one PC, whereas the traffic monitoring module runs on another PC in the on-board network. The resulting image data and evaluated traffic parameters are sent to a ground station in near real time and are distributed to the involved users. Thus, with the here suggested software/hardware system it becomes possible to support rescue forces and security forces in disaster areas or during mass events in near real time.

Automated DSM based Georeferencing of CARTOSAT-1 Stereo Scenes

Abstract: High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). A system for highly automated and operational DSM and orthoimage generation based on CARTOSAT-1 imagery is presented, with emphasis on fully automated georeferencing. The proposed system processes 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. In order to use the images for orthorectification or DSM generation, an affine RPC correction is required. This requires expensive and cumbersome GCP acquisition. In this paper, GCP are automatically derived from lower resolution reference datasets (Landsat ETM+ Geocover and SRTM DSM). The traditional method of collecting the lateral position from a reference image and interpolating the corresponding height from the DEM ignores the higher lateral accuracy of the SRTM dataset. Our method avoids this drawback by using a RPC correction based on DSM alignment, resulting in improved geolocation of both DSM and ortho images. The proposed method is part of an operational CARTOSAT-1 processor at Euromap GmbH for the generation of a high resolution European DSM. Checks against independent ground truth indicate a lateral error of 5-6 meters and a height accuracy of 1-3 meters.

Automatic generation of digital terrain models from Cartosat-1 stereo images

Abstract: This paper proposes a novel algorithm for automatic Digital Terrain Model (DTM) generation from high resolution CARTOSAT-1 satellite images generating accurate and reliable results. It consists of two major steps: Generation of Digital Surface Models (DSM) from CARTOSAT-1 stereo scenes and hierarchical image filtering for DTM generation. High resolution stereo satellite imagery is well suited for the creation of DSM. A system for automated and operational DSM and orthoimage generation based on CARTOSAT-1 imagery is presented, with emphasis on fully automated georeferencing. It processes level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. A novel, automatic georeferencing method is used to derive a high quality RPC correction from lower resolution reference dataset, such as Landsat ETM+ Geocover and SRTM C Band DSM. Digital surface models (DSM) are derived from dense stereo matching and forward intersection and subsequent interpolation into a regular grid. In the second step which is dedicated to DSM filtering, the DSM pixels are classified into ground and non-ground using the algorithm motivated from the gray-scale image reconstruction to suppress unwanted elevated pixels. In this method, non-ground regions, i.e., 3D objects as well as outliers (very low or very high elevated regions) are hierarchically separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Profiles in the image as well as a comparison of the derived DTM to the original data and ground truth data are presented. The ground truth data consist of a high quality DTM produced from aerial images which is generated by the Institut Cartografic de Catalunya (ICC) in Barcelona, Spain. The evaluation result indicates that almost all non-ground objects regardless of their size are eliminated and the iterative approach gives good results in hilly as well as smooth residential areas.

Method for geo-referencing of optical remote sensing images

Abstract: In the method for geo-referencing of optical remote sensing images of an area of the earth's surface, the geo-referencing is corrected based on an SAR image which is geo-referenced.

Traffic congestion parameter estimation in time series of airborne optical remote sensing images

Abstract: In this paper we propose a new model based traffic parameter estimation approach in congested situations in time series of airborne optical remote sensing data. The proposed approach is based on the combination of various techniques: change detection, image processing and incorporation of a priori information such as road network, information about vehicles and roads and finally a traffic model. The change detection in two images with a short time lag of several seconds is implemented using the multivariate alteration detection method resulting in a change image where the moving vehicles on the roads are highlighted. Further, image processing techniques are applied to derive the vehicle density in the binarized change image. Finally, this estimated vehicle density is related to the vehicle density, acquired by modelling the traffic flow for a road segment. The model is derived from a priori information about the vehicle sizes and road parameters, the road network and the spacing between the vehicles. Then, the modelled vehicle density is directly related to the average vehicle velocity on the road segment and thus the information about the traffic situation can be derived. To confirm our idea and to validate the method several flight campaigns with the DLR airborne experimental wide angle optical 3K digital camera system operated on a Do-228 aircraft were performed. Experiments are performed to analyse the performance of the proposed traffic parameter estimation method for highways and main streets in the cities. The estimated velocity profiles coincide qualitatively and quantitatively quite well with the reference measurements.

Matching of high resolution optical data to a shaded DEM

Abstract: One of the first essential steps in the analysis of satellite imagery is the orthorectification of the images. Orthorectification without ground control points (GCPs) using only the ephemeris and attitude data provided by the satellite operator provides an absolute accuracy of about 20 m to 1 km (depending on the satellite), which can be improved by measuring precise GCPs. In this paper, a method to obtain GCPs from an existing digital elevation model (DEM) is described and assessed. Since at least the SRTM DEM is available worldwide, DEMs could serve as a valuable additional source for the generation of GCPs. Furthermore, several planned and ongoing missions will increase the availability and accuracy of DEMs or stereo imagery respectively, e.g. ALOS, Tandem-X, etc.

On the Possibility of Intensity Based Registration for Metric Resolution SAR and Optical Imagery

Abstract: Multimodal image registration is a key to many remote sensing tasks like fusion, change detection, GIS overlay operations, 3D visualization etc. With advancements in research, intensity based similarity metrics namely mutual information (MI) and cluster reward algorithm (CRA) have been utilized for intricate multimodal registration problem. The computation of these metrics involves estimating the joint histogram directly from image intensity values, which might have been generated from different sensor geometries and/or modalities (e.g. SAR and optical). Modern day satellites like TerraSAR-X and IKONOS provide high resolution images generating enormous data volume along with very different image radiometric properties (especially in urban areas) not observed ever before. Thus, performance evaluation of intensity based registration techniques for metric resolution imagery becomes an interesting case study. In this paper, we analyze the performance of similarity metrics namely, mutual information and cluster reward algorithm for metric resolution images acquired over both plain and urban/semi-urban areas. Techniques for handling the generated enormous data volume and influence of really different sensor geometries over images especially acquired over urban areas have also been proposed and rightfully analyzed. Our findings from three carefully selected datasets indicate that the intensity based techniques can still be utilized for high resolution imagery but certain adaptations (like compression and segmentation) become useful for meaningful registration results.

Registration of High Resolution SAR and OpticalSatellite Imagery in Urban Areas

Abstract: With the launch of high resolution remote sensing satellites in different modalities like TerraSAR-X, WorldView-1 and Ikonos, the contribution of remote sensing for various applications has received a tremendous boost. Specifically, the combined analysis of high resolution SAR and optical imagery is of immense importance in monitoring and assessing catastrophes and natural disaster. Although, latest satellites provide georeferenced and orthorectified data products, still registration errors exist within images acquired from different sources. These need to be taken care off through quick automated techniques before the deployment of these data sources for remote sensing applications. Modern satellites like TerraSAR-X and Ikonos have further widened the existing gap of sensor geometry and radiometry between the two sensors. These satellites provide high resolution images generating enormous data volume along with very different image radiometric and geometric properties (especially in urban areas) leading to failure of multimodal similarity metrics like mutual information to detect the correct registration parameters. In this paper we present a processing chain to register high resolution SAR and optical images by combining feature based techniques namely, homogeneous regions extracted from high resolution images and intensity based similarity metrics namely normalized cross correlation and mutual information. Our test dataset consist of images from TerraSAR-X and Ikonos acquired over the city of Sichuan, China. First results from registration show good visual alignment of SAR and the optical image.

Analysis of Image Sequences for the Detection and Monitoring of Moving Traffic

Abstract: Analyse von Bildsequenzen zur Detektion und Uberwachung von fliesendem Verkehr. Dieser Artikel zielt auf das Erkennen und Verfolgen von Fahrzeugen aus luftgetragenen Bildsequenzen zur Uberwachung von fliesendem Verkehr. Zwei unterschiedliche Systeme werden beschrieben: Das Erste ist ein echtzeitnahes Tracking- Verfahren, welches auf der normalisierten Kreuzkorrelation basiert. Das zweite Verfahren bindet Modellwissen uber Fahrerverhalten, Verkehrsdynamiken und Kontext ein, um Geschwindigkeitsund Trajektorienbewertungen auszunutzen. Die Ergebnisse und die abgeleiteten Qualitatsmase zeigen die Leistungsfahigkeit der Systeme, insbesondere der Beitrag des Verkehrsmodellwissens erhoht die Korrektheit der Trackingergebnisse. Abschliesend wird ein Ausblick auf zukunftige Forschungen gegeben.

Change Detection Tools

Abstract: In this chapter a wide range of change detection tools is addressed. They are grouped into methods suitable for optical and multispectral data, synthetic aperture radar (SAR) images, and 3D data. Optical and multispectral methods include unsupervised approaches, supervised and knowledge-based approaches, pixel-based and object-oriented approaches, multivariate alteration detection, hyperspectral approaches, and approaches that deal with changes between optical images and existing vector data. Radar methods include constant false-alarm rate detection, adaptive filtering, multi-channel segmentation (an object-oriented approach), hybrid methods, and coherent change detection. 3D methods focus on tools that are able to deal with 3D information from ground based laser-ranging systems, LiDAR, and elevation models obtained from air/space borne optical and SAR data. Highlighted applications are landcover change, which is often one of the basic types of information to build analysis on, monitoring of nuclear safeguards, third-party interference close to infrastructures (or borders), and 3D analysis. What method to use is dependent on the sensor, the size of the changes in comparison with the resolution, their shape, textural properties, spectral properties, and behaviour in time, and the type of application. All these issues are discussed to be able to determine the right method, with references for further reading

Traffic Monitoring without single Car Detection from optical airborne Images

Abstract: This article describes several methods for traffic monitoring from airborne optical remote sensing data. These methods classify the traffic into free flowing traffic, traffic congestion and traffic jam. Furthermore a method is explained, which provides information about the average speed of dense traffic on a defined part of the road. All methods gather the information directly from image features, without the use of single vehicle detection. The classification of the traffic is done by stacking at least three overlapping images on top of each other and calculating the standard deviation of the gray values of each overlying pixel. In addition to that a texture analysis is implemented to differentiate the traffic. The average speed of dense traffic is calculated employing disparities of two following images of the same scene. All methods, which were presented in this article were tested on various data sets and compared with interactive measured reference data. These methods will be applicable in combination with methods using single car detection in the ARGOS-Project (AiRborne wide area hiGh altitude mOnitoring System).

Stereo evaluation of ALOS PRISM and IKONOS in Yemen

Abstract: DLR's Remote Sensing Technology Institute has a long lasting experience in developing spaceborne stereo scanners (MEOSS, MOMS) and the corresponding photogrammetric software systems for stereo evaluation and orthorectification. It takes part in the ESA/JAXA-AO Program to evaluate the performance and potential of the three-line stereo scanner PRISM and the multispectral imaging sensor AVNIR-2 on-board the Japanese satellite ALOS as a principal investigator. The high geometric resolution of PRISM (2.5 m ground sampling distance at nadir) combined with the medium swath width of 35 km has the potential to achieve high quality Digital Elevation Models up to 1:25.000 scale topographic maps for various applications. One of the proposed test sites is located near Sana'a, Yemen, where additionally to the PRISM stereo data also an IKONOS stereo image pair exists, which is used for DEM comparison and performance analysis. The results of this test site are evaluated in cooperation with the Advanced Data processing Research INstitute (ADRIN), India and the Federal Institute for Geosciences and Natural Resources (BGR), Hannover.

Refinement of urban digital elevation models from very high resolution stereo satellite images

Abstract: Digital elevation models (DEM) of high resolution and high quality are required for many applications like urban modeling, readiness for catastrophes or disaster assessment. A good source for the derivation of such DEMs from any place in the world are very high resolution (VHR) satellite stereo images as provided e.g. by Ikonos, QuickBird or WorldView. In this paper a method for the generation and refinement of urban high resolution DEMs from VHR imagery is presented and evaluated. Urban DEMs generated from very high resolution satellite imagery of ground sampling distances of about one meter are normally of resolutions of about three to ten meters. For the above mentioned applications of urban DEMs such results are often too coarse. In this paper an advanced method for the generation of dense digital elevation models is presented and discussed. The method is mainly based on dense stereo algorithms developed for computer vision applications. It is adapted and optimized to earth observation requirements. In the paper the DEM generation together with the additional refinement steps is presented and evaluated using very high resolution stereo imagery from Munich and Athens. The generated DEMs are compared to ground truth data where available and the quality and efficiency of the algorithms are analyzed and discussed.

Orthorectification and DSM generation with ALOS-Prism data in urban areas

Abstract: In this paper different methods for deriving digital surface models (DSM) from ALOS Prism three line stereo images are generated and analyzed. The methods used are classical hierarchical stereo matching with forward intersection and two different dense stereo methods. These are digital line warping which was derived from speech recognition algorithms and semi global matching which is originating in computer vision. All these dense stereo methods need epipolar imagery as input and provide so called disparity images as output. For this in a first step the Prism images has to be transformed by pairs to epipolar geometry. For the reprojection of the disparity images to real DSMs rational polynomial coefficients — which were computed from the satellite ephemeris and attitude date — are used. Finally the DSMs generated by all these different methods are compared to a DSM derived from an Ikonos stereo image pair with a ground sampling distance of 1 m.

Joint Use of Optical and SAR Data for Ship Detection

Abstract: The German Aerospace Center DLR and DigitalGlobe have been engaged in a modest R&D project to investigate complementary uses of Earth Observation (EO) and Radar data. Coordinated collections of TerraSAR-X and WorldView-1 data during July-August 2009 have been acquired. The near real time alerting/maritime situational awareness application has been tested. The imaging was performed within the same day. The low resolution, large swath radar image was used to detect ships and provide tip-off for high resolution EO imaging which in its turn provides alerts of on-going activity or actual ship ID/type. Data over Bandar Abbas, Iran and Persian Gulf have been acquired and analyzed. DLR own software SAINT was used for ship detection in radar images. The size and heading of a ship is estimated automatically. The velocity of a ship is estimated interactively using a radial velocity component, obtained from an azimuth displacement in radar image, and ship heading. Extracted information is used to predict ship position at the optical image acquisition time in order to provide an alert. First preliminary results are presented.

Using geometric accuracy of TerraSAR-X data for improvement of direct sensor orientation and ortho-rectification of optical sattelite data

Abstract: The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. It is due to the fact that it measures distances which are mainly dependent on the position of the satellite and the terrain height. If the used DEM is of high accuracy, the resulting geocoded data are very precise. This precision can be used to improve the exterior orientation and thereby the geometric accuracy of optical satellite data. The technique used is the measurement of identical points in the images, either by manual measurements or through local image matching using mutual information and to estimate improvements for the attitude data through this information. By adjustment calculations falsely matched points can be eliminated and an optimal improvement can be found. The optical data are orthorectified using these improvements and the available DEM. The results are compared using conventional ground control information from GPS measurements.