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

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

[no abstract available

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

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

Editorial note

[No abstract available

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