Persistent scatterer aided facade lattice extraction in single airborne optical oblique images

We present a new method to extract patterns of regular facade structures from single optical oblique images. To overcome the missing three-dimensional information we incorporate structural information derived from Persistent Scatter (PS) point cloud data into our method. Single oblique images and PS point clouds have never been combined before and offer promising insights into the compatibility of remotely sensed data of different kinds. Even though the appearance of facades is significantly different, many characteristics of the prominent patterns can be seen in both types of data and can be transferred across the sensor domains. To justify the extraction based on regular facade patterns we show that regular facades appear rather often in typical airborne oblique imagery of urban scenes. The extraction of regular patterns is based on well established tools like cross correlation and is extended by incorporating a module for estimating a window lattice model using a genetic algorithm. Among others the results of our approach can be used to derive a deeper understanding of the emergence of Persistent Scatterers and their fusion with optical imagery. To demonstrate the applicability of the approach we present a concept for data fusion aiming at facade lattices extraction in PS and optical data

Modeling spacecraft oscillations in hrsc images of mars express

Since January 2004 the High Resolution Stereo Camera (HRSC) is mapping planet Mars. The multi-line sensor on board the ESA Mission Mars Express images the Martian surface with a resolution of up to 1 2 m per pixel in three dimensions and in color. As part of the Photogrammetric/Cartographic Working Group of the HRSC Science Team the Institute of Photogrammetry and GeoInformation (IPI) of the Leibniz Universitat Hannover is involved in photogrammetrically processing the HRSC image data. To derive high quality 3D surface models, color orthoimages or other products, the accuracy of the observed position and attitude information in many cases should be improved. This is carried out via a bundle adjustment. In a considerable number of orbits the results of the bundle adjustment are disturbed by high frequency oscillations. This paper describes the impact of the high frequency angular spacecraft movement to the processing results of the last seven years of image acquisition and how the quality of the HRSC data products is significantly improved by modeling these oscillations.DLR/50 QM 090

Detection and 3D modelling of vehicles from terrestrial stereo image pairs

The detection and pose estimation of vehicles plays an important role for automated and autonomous moving objects e.g. in autonomous driving environments. We tackle that problem on the basis of street level stereo images, obtained from a moving vehicle. Processing every stereo pair individually, our approach is divided into two subsequent steps: the vehicle detection and the modelling step. For the detection, we make use of the 3D stereo information and incorporate geometric assumptions on vehicle inherent properties in a firstly applied generic 3D object detection. By combining our generic detection approach with a state of the art vehicle detector, we are able to achieve satisfying detection results with values for completeness and correctness up to more than 86%. By fitting an object specific vehicle model into the vehicle detections, we are able to reconstruct the vehicles in 3D and to derive pose estimations as well as shape parameters for each vehicle. To deal with the intra-class variability of vehicles, we make use of a deformable 3D active shape model learned from 3D CAD vehicle data in our model fitting approach. While we achieve encouraging values up to 67.2% for correct position estimations, we are facing larger problems concerning the orientation estimation. The evaluation is done by using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012).DFG/GRK/215

Gottfried Konecny: From draftsman to Professor emeritus - 75 years of involvement in photogrammetry and remote sensing

[no abstract available

Invariant descriptor learning using a Siamese convolutional neural network

In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95% recall rate on standard benchmark datasets

Automatic qualtiy control of cropland and grasland GIS objects using IKONOS satellite imagery

As a consequence of the wide-spread application of digital geo-data in Geoinformation Systems (GIS), quality control has become increasingly important. A high degree of automation is required in order to make quality control efficient enough for practical application. In order to achieve this goal we have designed and implemented a semi-automatic technique for the verification of cropland and grassland GIS objects using 1 m pan-sharpened multispectral IKONOS imagery. The approach compares the GIS objects and compares them with data derived from high resolution remote sensing imagery using image analysis techniques. Textural, structural, and spectral features are assessed in a classification based on Support Vector Machines (SVM) in order to check whether a cropland or grassland object in the GIS is correct or not. The approach is explained in detail, and an evaluation is presented using reference data. Both the potential and the limitations of the system are discussed.German Federal Agency for Cartography and Geodesy (BKG

A dynamic Bayes Network for visual pedestrian tracking

Many tracking systems rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach suggests a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, prior scene information, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forests-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available dataset captured in a challenging outdoor scenario. Using the adaptive classifier, our system is able to keep track of pedestrians over long distances while at the same time supporting the localisation of the people. The results show that the derived trajectories achieve a geometric accuracy superior to the one achieved by modelling the image positions as observations