Enhancment of dense urban digital surface models from VHR optical satellite stereo data by pre-segmentation and object detection

The generation of digital surface models (DSM) of urban areas from very high resolution (VHR) stereo satellite imagery requires advanced methods. In the classical approach of DSM generation from stereo satellite imagery, interest points are extracted and correlated between the stereo mates using an area based matching followed by a least-squares sub-pixel refinement step. After a region growing the 3D point list is triangulated to the resulting DSM. In urban areas this approach fails due to the size of the correlation window, which smoothes out the usual steep edges of buildings. Also missing correlations as for partly – in one or both of the images – occluded areas will simply be interpolated in the triangulation step. So an urban DSM generated with the classical approach results in a very smooth DSM with missing steep walls, narrow streets and courtyards. To overcome these problems algorithms from computer vision are introduced and adopted to satellite imagery. These algorithms do not work using local optimisation like the area-based matching but try to optimize a (semi-)global cost function. Analysis shows that dynamic programming approaches based on epipolar images like dynamic line warping or semiglobal matching yield the best results according to accuracy and processing time. These algorithms can also detect occlusions – areas not visible in one or both of the stereo images. Beside these also the time and memory consuming step of handling and triangulating large point lists can be omitted due to the direct operation on epipolar images and direct generation of a so called disparity image fitting exactly on the first of the stereo images. This disparity image – representing already a sort of a dense DSM – contains the distances measured in pixels in the epipolar direction (or a no-data value for a detected occlusion) for each pixel in the image. Despite the global optimization of the cost function many outliers, mismatches and erroneously detected occlusions remain, especially if only one stereo pair is available. To enhance these dense DSM – the disparity image – a pre-segmentation approach is presented in this paper. Since the disparity image is fitting exactly on the first of the two stereo partners (beforehand transformed to epipolar geometry) a direct correlation between image pixels and derived heights (the disparities) exist. This feature of the disparity image is exploited to integrate additional knowledge from the image into the DSM. This is done by segmenting the stereo image, transferring the segmentation information to the DSM and performing a statistical analysis on each of the created DSM segments. Based on this analysis and spectral information a coarse object detection and classification can be performed and in turn the DSM can be enhanced. After the description of the proposed method some results are shown and discussed

Derivation of building structures from noisy digital surface models

In this work we present a novel approach for segementation of a noisy DSM to building structures and other non-building structures - normally trees - and the modeling of them. Mostly Digital Surface Models (DSMs) from only a few aerial images or only from one pair of satellite images tend to be very noisy and lack good quality especially in shadow areas. Since actual methods for deriving roofs rely on a valid height information by joining areas of same slope to a roof-plane these fail regularly with such noisy DSMs. In our presented approach we use a slope map of the DSM only to detect flat regions. Since those regions on top of roofs are mostly good illuminated we can derive the ridges of roofs and flat roofs and also ground areas. All narrow, flat, elevated areas are ridges and may occur on roofs or on trees. After connecting ridges in ridge-directions there remain two types of ridges: long, straight ridges of roofs and mixed short ridges in many directions for the trees. Fitting symmetric planes through the roof-ridge-lines gives finally the roof-planes reducing the effects of noise on shadowed parts of the roof. Taking the other tree-ridges as seeds for a watershed transformation will give the trees. Finally the proposed method is applied to a noisy DSM and the results will be discussed

Extraction of Cloud Heights from Sentinel-2 Multispectral Images

Investigation of the focal plane assembly of the Sentinel-2 satellites show slight delays in the acqusition time of different bands on different CCD lines of about 0.5 to 1 second. This effect was already exploited in the detection of moving objects in very high resolution imagery as from WorldView-2 or -3 and also already for Sentinel-2 imagery. In our study we use the four 10-m-bands 2, 3, 4 and 8 (blue, green, red and near infrared) of Sentinel-2. In the level 1C processing each spectral band gets orthorectified separately on the same digital elevation model. So on the one hand moving objects on the ground experience a shift between the spectral bands. On the other hand objects not on the ground also show a slight shift between the spectral bands depending on the height of the object above ground. In this work we use this second effect. Analysis of cloudy Sentinel-2 scenes show small shifts of only one to two pixels depending on the height of the clouds above ground. So a new method based on algorithms for deriving dense digital elevation models from stereo imagery was developed to derive the cloud heights in Sentinel-2 images from the parallax from the 10-m-bands. After detailled description of the developed method it is applied to different cloudy Sentinel-2 images and the results are cross-checked using the shadows of the clouds together with the position of the sun at acquisition time

Prediction of Wind Speeds based on Digital Elevation Models using Boosted Regression Trees

In this paper a new approach is presented to predict maximum wind speeds using Gradient Boosted Regression Trees (GBRT). GBRT are a non-parametric regression technique used in various applications, suitable to make predictions without having an in-depth a-priori knowledge about the functional dependancies between the predictors and the response variables. Our aim is to predict maximum wind speeds based on predictors, which are derived from a digital elevation model (DEM). The predictors describe the orography of the Area-of-Interest (AoI) by various means like first and second order derivatives of the DEM, but also higher sophisticated classifications describing exposure and shelterness of the terrain to wind flux. In order to take the different scales into account which probably influence the streams and turbulences of wind flow over complex terrain, the predictors are computed on different spatial resolutions ranging from 30 m up to 2000 m. The geographic area used for examination of the approach is Switzerland, a mountainious region in the heart of europe, dominated by the alps, but also covering large valleys. The full workflow is described in this paper, which consists of data preparation using image processing techniques, model training using a state-of-the-art machine learning algorithm, in-depth analysis of the trained model, validation of the model and application of the model to generate a wind speed map

A new Approach to Hazard Analysis of Heavy Rainfall Events based on the Catchment Area of the Ahr River

In this summer a long time stationary rain event struck parts of western Germany leading to massive floodings - especially in the valley of the Ahr approximately 20 km south of Bonn. Such long-term stationary weather conditions get actually more and more frequent and can lead to long extreme heat or massive continous rainfall as shown in a study of the Potsdam-Institut für Klimafolgenforschung (PIK) this year. The flood of the Ahr revealed that the existing modelling for flood probabilities is not sufficient. Possible causes may be the comparatively short observation period of the underlying measurements, missing historical data or the dynamics of climate change are not taken into account. For this reason, our approach is based on simulations of individually adapted worst case scenarios to derive possible effects of heavy rainfall more generally and over a wide area. In the last years we developed a methodology for classification of strong rain dangers depending only on the terrain. We calculated strong rain danger maps covering hole Germany and Austria estimating a worst case scenario by not taking into account local drains since those are mostly blocked by leaves and branches at such sudden events. But these maps are only based on the influence of the direct surrounding in strong rain events and do not consider water coming from other areas. So we developed an additional component for including water-run-off from up-stream areas. In the presented study we calculate the maximum run-off for a whole water catchment area assuming a massive strong rain event and the following flash flood. For each position in the run-off-map a local height profile perpendicular to the flow direction is calculated and filled up with the maximum estimated water volume at this position. So cross sections along a river in a valley giving a maximum water level for the maximum possible run-off for a given strong rain event are derived. Since some part of the rain will drain away and not contribute to the run-off this is also a worst case estimation. The results are compared to aerial imagery acquired on 2021-07-16 - two days after the flooding struck the Ahr valley -, flood-masks derived from Sentinel-1 imagery and Copernicus damage assessment maps. Based on this imagery and measurements and estimations of water gauge levels we calculate the effective rain-height of the catchment and the simulation is calibrated and adapted to the observed water levels. Based on these results we can derive also an estimation of the flooding situation in the whole catchment area including tributary valleys