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

Thermal anomaly detection based on saliency analysis from multimodal imaging sources

Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy

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

Semi-supervised segmentation of concrete aggregate using consensus regularisation and prior guidance

In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to significantly improve the performance of purely supervised segmentation learning. However, the consensus principle behind consistency training has at least one drawback, which we identify in this paper: imbalanced label distributions within the data. To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge. Specifically, we propose a lightweight architecture consisting of a shared encoder and a main decoder, which is trained in a supervised manner. An auxiliary decoder is added as additional branch in order to make use of unlabelled data based on consensus training, and we add additional constraints derived from prior information on the class distribution and on auto-encoder regularisation. Experiments performed on our concrete aggregate dataset presented in this paper demonstrate the effectiveness of the proposed approach, outperforming the segmentation results achieved by purely supervised segmentation and standard consistency training

Mounting calibration of a multi-view camera system on a uav platform

Multi-view camera systems are used more and more frequently for applications in close-range photogrammetry, engineering geodesy and autonomous navigation, since they can cover a large portion of the environment and are considerably cheaper than alternative sensors such as laser scanners. In many cases, the cameras do not have overlapping fields of view. In this paper, we report on the development of such a system mounted on a rigid aluminium platform, and focus on its geometric system calibration. We present an approach for estimating the exterior orientation of such a multi-camera system based on bundle adjustment. We use a static environment with ground control points, which are related to the platform via a laser tracker. In the experimental part, the precision and partly accuracy that can be achieved in different scenarios is investigated. While we show that the accuracy potential of the platform is very high, the mounting calibration parameters are not necessarily precise enough to be used as constant values after calibration. However, this disadvantage can be mitigated by using those parameters as observations and refining them on-the-job

Towards better classification of land cover and land use based on convolutional neural networks

Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the land cover and to classify land use objects using convolutional neural networks (CNN). High-resolution aerial images and derived data such as digital surface models serve as input. An encoder-decoder based CNN is used for land cover classification. We found a composite including the infrared band and height data to outperform RGB images in land cover classification. We also propose a CNN-based methodology for the prediction of land use label from the geospatial databases, where we use masks representing object shape, the RGB images and the pixel-wise class scores of land cover as input. For this task, we developed a two-branch network where the first branch considers the whole area of an image, while the second branch focuses on a smaller relevant area. We evaluated our methods using two sites and achieved an overall accuracy of up to 89.6% and 81.7% for land cover and land use, respectively. We also tested our methods for land cover classification using the Vaihingen dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 90.7%. © Authors 2019

Marked point processes for the automatic detection of bomb craters in aerial wartime images

Many countries were the target of air strikes during the Second World War. The aftermath of such attacks is felt until today, as numerous unexploded bombs or duds still exist in the ground. Typically, such areas are documented in so-called impact maps, which are based on detected bomb craters. This paper proposes a stochastic approach to automatically detect bomb craters in aerial wartime images that were taken during World War II. In this work, one aspect we investigate is the type of object model for the crater: we compare circles with ellipses. The respective models are embedded in the probabilistic framework of marked point processes. By means of stochastic sampling the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function which describes the conformity with a predefined model. High gradient magnitudes along the border of the object are favoured and overlapping objects are penalized. In addition, a term that requires the grey values inside the object to be homogeneous is investigated. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global optimum of the energy function. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results, based on 22 aerial wartime images, show the general potential of the method for the automated detection of bomb craters and the subsequent automatic generation of an impact map. © Authors 2019

Accurate matching and reconstruction of line features from ultra high resolution stereo aerial images

In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned(<= 10 degrees) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen-Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line