Registration of High Resolution SAR and Optical Satellite Imagery in Urban Areas

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

Automatic Vehicle Detection in Aerial Image Sequences of Urban Areas using 3d Hog Features

With the development of low cost aerial optical sensors having a spatial resolution in the range of few centimetres, the traffic monitoring by plane receives a new boost. The gained traffic data are very useful in various fields. Near real-time applications in the case of traffic management of mass events or catastrophes and non time critical applications in the wide field of general transport planning are considerable. A major processing step for automatically provided traffic data is the automatic vehicle detection. In this paper we present a new processing chain to improve this task. First achievement is limiting the search space for the detector by applying a fast and simple pre-processing algorithm. Second achievement is generating a reliable detector. This is done by the use of HoG features (Histogram of Oriented Gradients) and their appliance on two consecutive images. A smart selection of this features and their combination is done by the Real AdaBoost (Adaptive Boosting) algorithm. Our dataset consists of images from the 3K camera system acquired over the city of Munich, Germany. First results show a high detection rate and good reliability

Automatic Registration of High Resolution SAR and Optical Satellite Imagery in Urban Areas

The work presented in this thesis addresses the problem of registration of high resolution SAR and optical satellite imagery in dense urban areas. With the launch of high resolution remote sensing satellites in different modalities like TerraSAR-X, WorldView-1 and IKONOS-2, 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 or through quick automated techniques before the deployment of these data sources for remote sensing applications. Different registration methods like the ones based on detecting geometrical features like interest points and line detectors to intensity based strategies have been rightfully evaluated. Considering the meticulous task of extracting and matching conjugate features in SAR and optical imagery (especially metric resolution imagery) a general feature based image registration technique for various scenarios might be difficult to develop and implement and therefore region based approach has been selected in this thesis. In general, for high resolution satellite images acquired over urban areas, common city features like wide roads, rivers, big stadiums, play grounds, parks can be expected to appear in considerable sizes and be represented by relatively homogeneous intensity values and therefore the explored strategies in this thesis are based on region detection which in particular show a robust performance for high resolution SAR images. The scheme here is to detect 'on ground' regions, which are not expected to be affected by very different SAR and optical sensor geometries. The following step selects the extracted regions and prepares them for matching with methods based on cross-correlation and mutual information. Selected datasets for the testing and evaluation of the region based registration methods are pre- and post-disaster (2008 earthquake in Sichuan China) IKONOS-2 images along with a TerraSAR-X image acquired within hours after the disastrous earthquake. The evaluated automated methods in this thesis have successfully detected shift parameters within already orthorectified SAR and optical imagery acquired over dense urban areas

Motion component supported Boosted Classifier for car detection in aerial imagery

Research of automatic vehicle detection in aerial images has been done with a lot of innovation and constantly rising success for years. However information was mostly taken from a single image only. Our aim is using the additional information which is offered by the temporal component, precisely the difference of the previous and the consecutive image. On closer viewing the moving objects are mainly vehicles and therefore we provide a method which is able to limit the search space of the detector to changed areas. The actual detector is generated of HoG features which are composed and linearly weighted by AdaBoost. Finally the method is tested on a motorway section including an exit and congested traffic near Munich, Germany

Car detection in low frame-rate aerial imagery of dense urban areas

Knowledge about quantity and position of moving and stationary vehicles is essential for traffic management and planning. This information can be used, for instance, for security of mass events or to support rescue crews in disaster situations. In order to get this information, large areas have to be examined quickly and completely. Very suitable for this task are airborne optical sensors. However, a reliable automatic method to locate vehicles in aerial images is necessary. In the present work a method for automatic extraction of vehicles in urban areas is presented. The work mainly covers three key fields of car detection. The first is related to the extraction of ground areas. On the assumption that trafficable areas are often ground areas in densely populated cities, disparity maps are calculated using the semi-global matching algorithm (SGM). Subsequently, a threshold is automatically determined to separate ground from non-ground regions (Minimum Error Thresholding). The second field concerns the introduction of a object-based method for extracting car candidates. In order to do this, the image is smoothed using the mean curvature flow, and a region-growing algorithm is then applied. The regions obtained are considered autonomous regions and are filtered multiple times with regard to their geometric properties. The third field is the examination of the remaining candidate regions by a classifier based on gradients (HOG features), which is trained by a machine learning algorithm (AdaBoost). However, the classifier is trained using only a few training samples. The goal is to minimize the manual effort and to provide a high degree of generalization. Thus, a strategy is presented which combines object-based and gradient-based techniques. The strategy is tested with five urban images from the 3K+ camera system and the UltraCam Eagle camera system, with 13 cm and 20 cm GSD, respectively. Through the use of disparity maps, it is shown that the car detection quality in densely populated inner-city areas can be enhanced. Objects on the top of buildings are now accurately excluded from the detection process. Furthermore, the car detection approach presented is able to detect cars in different datasets without adjustment of parameter settings (different sensors and different resolution). The results of detection show that a completeness of 80% leads to a correctness of 65% to 95%

Validation of Vehicle Candidate Areas in Aerial Images Using Color Co-Occurrence Histograms

Traffic monitoring plays an important role in transportation management. In addition, airborne acquisition enables a flexible and realtime mapping for special traffic situations e.g. mass events and disasters. Also the automatic extraction of vehicles from aerial imagery is a common application. However, many approaches focus on the target object only. As an extension to previously developed car detection techniques, a validation scheme is presented. The focus is on exploiting the background of the vehicle candidates as well as their color properties in the HSV color space. Therefore, texture of the vehicle background is described by color co-occurrence histograms. From all resulting histograms a likelihood function is calculated giving a quantity value to indicate whether the vehicle candidate is correctly classified. Only a few robust parameters have to be determined. Finally, the strategy is tested with a dataset of dense urban areas from the inner city of Munich, Germany. First results show that certain regions which are often responsible for false positive detections, such as vegetation or road markings, can be excluded successfully

Airborne traffic monitoring supported by fast calculated digital surface models

Vehicle detection in dense urban areas is often complicated due to car-like objects on rooftops which result in false positive detections. This can be easily avoided by using a digital surface model (DSM) calculated from two consecutive images to exclude those regions. However, in the real-time case traffic information has to be gathered rapidly and the calculation of the DSM for the whole image takes a lot of time. The presented approach suggest a method where the disparity image is only calculated for areas of interest. These areas are selected by projecting the road segments from a road database in the original image using the collinearity equation. The local coordinates of the detected vehicles are then transformed back in the UTM coordinate system using the collinearity equation again. It can be shown that the search area for the detector is significantly reduced and which also leads to improved results of the detection

Airborne vehicle detection in dense urban areas using HoG features and disparity maps

Vehicle detection has been an important research field for years as there are a lot of valuable applications, ranging from support of traffic planners to real-time traffic management. Especially detection of cars in dense urban areas is of interest due to the high traffic volume and the limited space. In city areas many car-like objects (e.g., dormers) appear which might lead to confusion. Additionally, the inaccuracy of road databases supporting the extraction process has to be handled in a proper way. This paper describes an integrated real-time processing chain which utilizes multiple occurrence of objects in images. At least two subsequent images, data of exterior orientation, a global DEM, and a road database are used as input data. The segments of the road database are projected in the non-geocoded image using the corresponding height information from the global DEM. From amply masked road areas in both images a disparity map is calculated. This map is used to exclude elevated objects above a certain height (e.g., buildings and vegetation). Additionally, homogeneous areas are excluded by a fast region growing algorithm. Remaining parts of one input image are classified based on the ‘Histogram of oriented Gradients (HoG)’ features. The implemented approach has been verified using image sections from two different flights and manually extracted ground truth data from the inner city of Munich. The evaluation shows a quality of up to 70 percent

General Mathematical Model of Least Squares 3D surface matching and its Application of strip adjustment

Systematic errors in point clouds acquired by airborne laser scanners, photogrammetric methods or other 3D measurement techniques need to be estimated and removed by adjustment procedures. The proposed method estimates the transformation parameters between reference surface and registration surface using a mathematical adjustment model. 3D surface matching is an extension of 2D least squares image matching. The estimation model is a typical Gauss-Markoff model and the goal is minimizing the sum of squares of the Euclidean distances between the contiguous surfaces. Besides the generic mathematical model, we also propose a concept of conjugate points rules which are suitable for special registering applications, and compare it to three typical conjugate points rules. Finally, we explain how this method can be used for the co-registration of real 3D point sets and show co-registration results based on airborne laser scanner data. Concluding results of our experiment suggest that the proposed method has a good performance of 3D surface matching, and the least normal distance rule returns the best result for the strip adjustment of airborne laser altimetry data